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Detection of A Shadow of Animated Video Frames in RGB Color Space
Available online at www.sciencedirect.com
ScienceDirect ScienceDirect
Procedia Computer Science 00 (2018) 000–000 Procedia Computer Science 00 (2018) 000–000
Available online at www.sciencedirect.com
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Computer Science 132 (2018) 103–108
International Conference on Computational Intelligence and Data Science (ICCIDS 2018) International Conference on Computational Intelligence and Data Science (ICCIDS 2018)
Detection of A Shadow of Animated Video Frames in RGB Color Detection of A Shadow of Animated Video Frames in RGB Color Space Space Kuldip Acharyaaa, Dibyendu Ghoshalbb Kuldip Acharya , Dibyendu Ghoshal
a Computer Science and Engineering Department, National Institute of Technology Agartala, Tripura (West), Pin:799046, INDIA. a Dept. of Electronics Engineering, National Institute of Technology Agartala, Tripura (West), Pin: 799046, INDIA. Computer Scienceand andCommunication Engineering Department, National Institute of Technology Agartala, Tripura (West), Pin:799046, INDIA. b Dept. of Electronics and Communication Engineering, National Institute of Technology Agartala, Tripura (West), Pin: 799046, INDIA. b
Abstract Abstract The study is made on the detection of a shadow of animated video frames in RGB color space. The present study is based on color invariant shadow detection accurately as far practicable theinlimitation of space. the Matlab software. Theis grayscale The study is made on the detection of a shadow of as animated videowithin frames RGB color The present study based on histogram is obtained to detection extract segments of the The mainwithin aim ofthe the limitation study is toof accurately visualize the shadows of the color invariant shadow accurately as shadows. far as practicable the Matlab software. The grayscale animated objects in consecutive animated of video frames. Such algorithm can be to detect the shadows of real-time histogram is obtained to extract segments the shadows. Thetype mainofaim of the study is applied to accurately visualize the shadows of the images. The advantages of shadow detection areframes. to find Such out the timing of the day is caused by Sun, to detect of thereal-time moving animated objects in consecutive animated video type of algorithm canwhich be applied to detect the shadows object shadow can be used, just to deceive the are enemies from can isbecaused used. by Sun, to detect the moving images. The advantages of shadow detection to find out the the target timingobject of theshadows day which object shadow can be used, just to deceive the enemies from the target object shadows can be used. © © 2018 2018 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/) © 2018 The under Authors. Published by B.V. committee of the International Conference on Computational Intelligence and Peer-review responsibility of Elsevier the scientific Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science (ICCIDS 2018). Data Science (ICCIDS 2018). Data Science (ICCIDS 2018). Keywords: Animation; Object detection; Shadow; Matlab; Video frames. Keywords: Animation; Object detection; Shadow; Matlab; Video frames.
1. Introduction 1. Introduction Natural catastrophes such as earth tremor, storm, volcano eruption and flood origin an excessive harm to an area. Natural catastrophes such isasstill earth tremor,tostorm, eruption and flood an excessive harm to network an area. Inevitable and un-forcing possible reducevolcano the problems afterward. In origin this process communication Inevitable and un-forcing is still the problems afterward. In this process communication network can be spoiled. These failures canpossible restrict to thereduce information flow from the natural catastrophe affected regions. These can be spoiled. These caninrestrict the information flow from the natural catastrophe regions. These videos appearance arefailures changes resolution, sensor category, alignment, superiority, andaffected ambient illumination videos appearance are changes resolution, sensor category, and ambient illumination circumstances. Shadow detectioninhas become an important part alignment, of imagerysuperiority, due to its potential of providing the circumstances. Shadow detection hasis become important of imagery to shadows its potential of providing the daytime information when Sunlight present. an Aerial imagespart always producedue some of any object which daytime whenbuildings, Sunlight trees, is present. Aerial images always produce some of any object which may varyinformation from stationary and any smaller objects. In the wartime, theshadows enemy bomber airplanes can may vary from stationary buildings, trees, and any smaller objects. In the wartime, the enemy bomber airplanes can Corresponding Author: [email protected] Corresponding Author: [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier B.V. Peer-review underThe responsibility of theby scientific of the International Conference on Computational Intelligence and 1877-0509 © 2018 Authors. Published Elsevier committee B.V.
Data Scienceunder (ICCIDS 2018). of the scientific committee of the International Conference on Computational Intelligence and Peer-review responsibility Data Science (ICCIDS 2018). 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science (ICCIDS 2018). 10.1016/j.procs.2018.05.168
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be deceived by creating various shadows created by multiple sources of height apart from Sunlight. During night times in the absence of the Sun, various artificial light sources can be used to create pseudo shadows and overlapping images. The underlying problem is that the shadows should be detected accurately and in proper directions. Already few research studies have been done [1-2] on shadow detections. Some of them based the shadow on a measure disaster the management in the present study, the focus is given on the detection of the shadow of the object in more accurate angle direction. The aim of the proposed study is to measure the shadow accurately, especially in the angular direction. The effect of the overlapping of various shadows is studied of a single object when multiple sources of light are used. To measure the time of the day very accurately by using any arbitrary object. The aim of the proposed study is to measure the shadow accurately, especially in the angular direction. The effect of the overlapping of various shadows is studied of a single object when multiple sources of light are used. 2. Literature Review In the study of associated research papers, it is observed and investigated that, scholars utilized the shadow information to perceive the buildings. Nevatia and Huertas [3] proposed a method to find out the relationship between houses and shadows. Their method initially applied to the images to obtain the corners. The obtained corners are categories as either shadow corners or bright corners and to form rectangles, bright corners have been utilized. Building hypothesis is ensured by using shadow corners for these rectangles. Thresholding method is utilized by McKeown et al. [4] to find out shadows in aerial images. The simulation result proved that shades of buildings and their borders contain vital information regarding heights of the building and shapes of roofs. Basic models and multiple cues are integrated by the proposed method of Zimmerman [5], to search the buildings. He utilized edge information, color, texture, shadow information to find out the buildings. His proposed methodology extracts buildings utilizing the blob detection technique. HCV, YIQ, HSI, HSV, and YCBCR models and other color spaces are compared by Tsai [6] for recognition of shadows in atmospherically pictures. Vu et al. [7] also utilized shadows information to locate buildings. Their method proved that information of shadow can be utilized to determine the damages and changes in buildings. The proposed method of Chen and Hutchinson [8] is able to perceive damages of destructive houses, buildings utilizing satellite pictures of bitemporal grayscale. First, two images are compared to find out the pixel-based changes. Then, they utilized a probabilistic approach to the extracted object. Beril Sirmacek and Cem Unsalan [1] proposed a method for detecting building, it's edge information and to detect building shapes from aerial images utilizing invariant shadow information and color features. Their method utilized invariant color features to find the areas of interest and to extract shadow information. Shadow segmentations is utilized to verify building locations and to determine the illumination direction. Support Vector Machine (SVM) based [9] method on gradient discipline and shade saliency space is utilized to hit upon shadows for illumination flexibility on the road. Shadows on the street constantly create a problem on perception obligations like detecting the object on the road and navigation by visualizing methods. Nonlinear SVM classifier is utilized in shadowed areas for outstanding and diagnosed via reconstructing road shadow descriptor. Color saliency space and gradient data are analyzed to put in force this operation. Computer vision packages [10] are frequently facing challenges in object detection, category, popularity and tracking a machine due to shadow impact and this interrupt in detection to human gait recognition, agility perception, and so forth. A fuzzy rule-based model utilizing variant residences together with the ratio of Red channel spectral from RGB, with a distinction in chromaticity color space, and average image depth is applied for self-shadow and forged shadow detection. A study on shadow discovery methods utilizing multi-light sources suggests upon a territory [11] when the mild from a supply can't reap the place due to obstacle via a query. The shadows play a critical role in giving essential data of an object of interest. In any case, they cause an issue in computer vision software, as an example, division, query region, and protest numbering. Accordingly, shadow reputation and evacuation are prepreparing undertakings in several computer vision packages. Multi-mild shadow identity method with expulsion techniques utilizing a mixture of tri-shading version, pressure version and LAB shading area for a discovery of various shadow has been done. Computer vision and image analysis application are affected by shadow [12] and it causes some unwanted changes in object extraction, segmentation and recognition because of the inappropriate
Kuldip Acharya et al. / Procedia Computer Science 132 (2018) 103–108 Kuldip Acharya et al. / Procedia Computer Science 00 (2018) 000–000
105
classification of shadow points as object points or foreground. So, shadow detection is a challenging task in computer vision application. Appropriate shadow detection is a difficult task due to the variation in light of the background and resemblance between the presence of the objects and background. Many algorithms and techniques have been introduced for the various natural situations to identify shadow from the images. 3. Methodology The angle of illumination for creating a shadow is not always a fixed one but it may be drifted due to the shifting the source of light, maybe Sunlight or any artificial source of light. These angles have a great impact on the shadow of the object. This angular variation is realized by an overlapping shadow of the same object and this shadowed area then becomes and overlapping hazy area. The uncertainty of the shape of the shadow creates some confusion during visualization. These effects can be utilized to measure the shadow accurately, especially in angular direction and to measure the time of the day very accurately by using any arbitrary object.
r
R G arctan RG 4
(1)
The converse of the tangent function gives the arctan function. It gives the angle whose tangent is a conferred digit. Here, R denotes red color, G denotes green color. In Taylor’s Theorem, if f is a continuous function and it is differentiable for n times which interval is [x, x + h], then some point exists there in this interval, which denoted by x + λh for some λ ∈ [0, 1], such that
f ( x h ) f ( x) hf ' ( x)
h2 '' h( n1) ( n 1) hn n f ( x) ... f f ( x h) ( x) n! 2 (n 1)!
(2)
Here, the angular resolution of the shadow depends on the direction of the source of the light. A slight change of direction of light source creates the change of angular shadow of the object. The shadow is a function of the directive angle of the light source. This function changes when the angle changes. x = theta and h= delta. Here, theta= 0.3 If f is a well-known analytic function where remains the derivatives of all orders, then one may think to grow the value of n indefinitely. Due to the correlation between red (R) and green (G) components and with the inherent correlation between the energy part and color component (RGB) in RGB color space, the formula may be rewritten as: 3.1. Proposed method
where,
p
p
4
R G tan 1 p RG
(3)
is the angle of ambiguity due to the above-mentioned correlation.
4. Results and Analysis The shadow of a human being is generated by animated video and it can be annotated a real shadow. The shadow is generated by a single source of light as shown in the frames of video frames. It is seen that the location of the shadow depends on the location of the source of light. In the animated video frame, more than one light sources are 3
Kuldip Acharya et al. / Procedia Computer Science 132 (2018) 103–108 Kuldip Acharya et al. / Procedia Computer Science 00 (2018) 000–000
106
used and it is observed more than one is created and they are seen to be overlapped. When the source of light is moved by a very small angle the shadows are observed to be shifted in angular directions. From two consecutive such image frames, the angular shift in the location of the shadows can be calculated by using Euclidian geometry. The shift in angle may be used to find the angular movement of the lights also. In normal daylight, the length, breadth of the shadow depends on the time of the day. Hence the shifting angle can be used to measure the timing of the day. The overlap angle can also be utilized to confuse and disorient the enemies’ bomber airplanes and this concept is valid for any object to consider as a target.
(a)
(b)
(a)
(c)
(e)
(d)
(f)
Fig. 1. (a) (b) Original video frames; (c) (d) Shadows of detected objects are shown by shadow boundaries; (e) Shadow mask; (f) Shadow ratio, colormap, and color bar.
Kuldip Acharya et al. / Procedia Computer Science 00 (2018) 000–000
Kuldip Acharya et al. / Procedia Computer Science 132 (2018) 103–108
(a)
(c)
107
(b)
(d)
Fig. 2. (a) (c) Original color invariants method [1]; (b) (d) Propose modified method gives more prominent results.
5. Conclusion and Future Work Detection of a shadow of animated video frames in RGB color space effect of the single and multiple light source on the formation of the shadow of an object has been studied. The results obtained are related to the benefit of the mankind in terms of the safety measure from the enemies and to find out the daytime found in an animated video environment. The Taylor series expansion and concept of offset angle arising out of the correlation between RGB component has also been taken account in the present study to detect the shadows of the moving objects in consecutive animated video frames. The proposed algorithm applied to three-dimensional animated images and simulated in Matlab software. The simulation results show that the methods propose modified method gives more prominent results than original color invariants method to detect the shadows. In future, fuzzy adaptive measures for real-time shadow detection from animated videos will be considered for more accurate results.
5
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Acknowledgments Author one acknowledges National Institute of Technology Agartala, India. Dedication Author one dedicates his work to his loving father Dr. Kalidas Acharya. References [1] B. Sirmacek and C. Unsalan. (2009) "Damaged Building Detection in Aerial Images using Shadow Information", 4th International Conference on Recent Advances in Space Technologies RAST 2009, Istanbul, Turkey. [2] C. Unsalan and K. L. Boyer. (2004) "Linearized vegetation indices based on a formal statistical framework," IEEE Transactions on Geoscience and Remote Sensing, vol. 42: 1575-1585. [3] A. Huertas and R. Nevatia. (1988) “Detecting buildings in aerial images,” Computer Vision, Graphics and Image Processing, vol. 41: 131– 152. [4] R. B. Irvin and D. M. McKeown. (1989) “Methods for exploiting the relationship between buildings and their shadows in aerial imagery,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 19, no. 1: 1564–1575. [5] P. Zimmermann. (2000) “A new framework for automatic building detection analyzing multiple cue data,” in International Archives of Photogrammetry and Remote Sensing IAPRS’, vol. 33: 1063–1070. [6] V. J. D. Tsai. (2006) “A comparative study on shadow compensation of color aerial images in invariant color models,” IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 6: 1661–1671. [7] T. Vu, M. Matsouka, and F. Yamazaki. (2004) “Shadow analysis in assisting damage detection due to earthquake from quickbird imagery,” Proceedings of the 10th international society for photogrammetry and remote sensing congress: 607–611. [8] Z. Chen and T. Hutchinson. (2007) “A probabilistic classification framework for urban structural damage estimation using satellite images,” Urban Remote Sensing Joint Event 2007: 1–7. [9] C. Wang, L. Deng, Z. Zhou, M. Yang and B. Wang. (2017) "Shadow detection and removal for illumination consistency on the road," 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), Shenzhen, China: 198-203. [10] S. Das and S. Meher. (2017) "A novel shadow detection method using fuzzy rule based model," 2017 IEEE 15th Student Conference on Research and Development (SCOReD), Wilayah Persekutuan Putrajaya, Malaysia: 493-498. [11] S. J. Patel, S. D. Degadwala and K. S. Shekokar. (2017) "A survey on multi light source shadow detection techniques," 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore: 1-4. [12] M. H. Panchal and N. C. Gamit. (2016) "A comprehensive survey on shadow detection techniques," 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai: 2249-2253.
ScienceDirect ScienceDirect
Procedia Computer Science 00 (2018) 000–000 Procedia Computer Science 00 (2018) 000–000
Available online at www.sciencedirect.com
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Computer Science 132 (2018) 103–108
International Conference on Computational Intelligence and Data Science (ICCIDS 2018) International Conference on Computational Intelligence and Data Science (ICCIDS 2018)
Detection of A Shadow of Animated Video Frames in RGB Color Detection of A Shadow of Animated Video Frames in RGB Color Space Space Kuldip Acharyaaa, Dibyendu Ghoshalbb Kuldip Acharya , Dibyendu Ghoshal
a Computer Science and Engineering Department, National Institute of Technology Agartala, Tripura (West), Pin:799046, INDIA. a Dept. of Electronics Engineering, National Institute of Technology Agartala, Tripura (West), Pin: 799046, INDIA. Computer Scienceand andCommunication Engineering Department, National Institute of Technology Agartala, Tripura (West), Pin:799046, INDIA. b Dept. of Electronics and Communication Engineering, National Institute of Technology Agartala, Tripura (West), Pin: 799046, INDIA. b
Abstract Abstract The study is made on the detection of a shadow of animated video frames in RGB color space. The present study is based on color invariant shadow detection accurately as far practicable theinlimitation of space. the Matlab software. Theis grayscale The study is made on the detection of a shadow of as animated videowithin frames RGB color The present study based on histogram is obtained to detection extract segments of the The mainwithin aim ofthe the limitation study is toof accurately visualize the shadows of the color invariant shadow accurately as shadows. far as practicable the Matlab software. The grayscale animated objects in consecutive animated of video frames. Such algorithm can be to detect the shadows of real-time histogram is obtained to extract segments the shadows. Thetype mainofaim of the study is applied to accurately visualize the shadows of the images. The advantages of shadow detection areframes. to find Such out the timing of the day is caused by Sun, to detect of thereal-time moving animated objects in consecutive animated video type of algorithm canwhich be applied to detect the shadows object shadow can be used, just to deceive the are enemies from can isbecaused used. by Sun, to detect the moving images. The advantages of shadow detection to find out the the target timingobject of theshadows day which object shadow can be used, just to deceive the enemies from the target object shadows can be used. © © 2018 2018 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/) © 2018 The under Authors. Published by B.V. committee of the International Conference on Computational Intelligence and Peer-review responsibility of Elsevier the scientific Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science (ICCIDS 2018). Data Science (ICCIDS 2018). Data Science (ICCIDS 2018). Keywords: Animation; Object detection; Shadow; Matlab; Video frames. Keywords: Animation; Object detection; Shadow; Matlab; Video frames.
1. Introduction 1. Introduction Natural catastrophes such as earth tremor, storm, volcano eruption and flood origin an excessive harm to an area. Natural catastrophes such isasstill earth tremor,tostorm, eruption and flood an excessive harm to network an area. Inevitable and un-forcing possible reducevolcano the problems afterward. In origin this process communication Inevitable and un-forcing is still the problems afterward. In this process communication network can be spoiled. These failures canpossible restrict to thereduce information flow from the natural catastrophe affected regions. These can be spoiled. These caninrestrict the information flow from the natural catastrophe regions. These videos appearance arefailures changes resolution, sensor category, alignment, superiority, andaffected ambient illumination videos appearance are changes resolution, sensor category, and ambient illumination circumstances. Shadow detectioninhas become an important part alignment, of imagerysuperiority, due to its potential of providing the circumstances. Shadow detection hasis become important of imagery to shadows its potential of providing the daytime information when Sunlight present. an Aerial imagespart always producedue some of any object which daytime whenbuildings, Sunlight trees, is present. Aerial images always produce some of any object which may varyinformation from stationary and any smaller objects. In the wartime, theshadows enemy bomber airplanes can may vary from stationary buildings, trees, and any smaller objects. In the wartime, the enemy bomber airplanes can Corresponding Author: [email protected] Corresponding Author: [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier B.V. Peer-review underThe responsibility of theby scientific of the International Conference on Computational Intelligence and 1877-0509 © 2018 Authors. Published Elsevier committee B.V.
Data Scienceunder (ICCIDS 2018). of the scientific committee of the International Conference on Computational Intelligence and Peer-review responsibility Data Science (ICCIDS 2018). 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science (ICCIDS 2018). 10.1016/j.procs.2018.05.168
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be deceived by creating various shadows created by multiple sources of height apart from Sunlight. During night times in the absence of the Sun, various artificial light sources can be used to create pseudo shadows and overlapping images. The underlying problem is that the shadows should be detected accurately and in proper directions. Already few research studies have been done [1-2] on shadow detections. Some of them based the shadow on a measure disaster the management in the present study, the focus is given on the detection of the shadow of the object in more accurate angle direction. The aim of the proposed study is to measure the shadow accurately, especially in the angular direction. The effect of the overlapping of various shadows is studied of a single object when multiple sources of light are used. To measure the time of the day very accurately by using any arbitrary object. The aim of the proposed study is to measure the shadow accurately, especially in the angular direction. The effect of the overlapping of various shadows is studied of a single object when multiple sources of light are used. 2. Literature Review In the study of associated research papers, it is observed and investigated that, scholars utilized the shadow information to perceive the buildings. Nevatia and Huertas [3] proposed a method to find out the relationship between houses and shadows. Their method initially applied to the images to obtain the corners. The obtained corners are categories as either shadow corners or bright corners and to form rectangles, bright corners have been utilized. Building hypothesis is ensured by using shadow corners for these rectangles. Thresholding method is utilized by McKeown et al. [4] to find out shadows in aerial images. The simulation result proved that shades of buildings and their borders contain vital information regarding heights of the building and shapes of roofs. Basic models and multiple cues are integrated by the proposed method of Zimmerman [5], to search the buildings. He utilized edge information, color, texture, shadow information to find out the buildings. His proposed methodology extracts buildings utilizing the blob detection technique. HCV, YIQ, HSI, HSV, and YCBCR models and other color spaces are compared by Tsai [6] for recognition of shadows in atmospherically pictures. Vu et al. [7] also utilized shadows information to locate buildings. Their method proved that information of shadow can be utilized to determine the damages and changes in buildings. The proposed method of Chen and Hutchinson [8] is able to perceive damages of destructive houses, buildings utilizing satellite pictures of bitemporal grayscale. First, two images are compared to find out the pixel-based changes. Then, they utilized a probabilistic approach to the extracted object. Beril Sirmacek and Cem Unsalan [1] proposed a method for detecting building, it's edge information and to detect building shapes from aerial images utilizing invariant shadow information and color features. Their method utilized invariant color features to find the areas of interest and to extract shadow information. Shadow segmentations is utilized to verify building locations and to determine the illumination direction. Support Vector Machine (SVM) based [9] method on gradient discipline and shade saliency space is utilized to hit upon shadows for illumination flexibility on the road. Shadows on the street constantly create a problem on perception obligations like detecting the object on the road and navigation by visualizing methods. Nonlinear SVM classifier is utilized in shadowed areas for outstanding and diagnosed via reconstructing road shadow descriptor. Color saliency space and gradient data are analyzed to put in force this operation. Computer vision packages [10] are frequently facing challenges in object detection, category, popularity and tracking a machine due to shadow impact and this interrupt in detection to human gait recognition, agility perception, and so forth. A fuzzy rule-based model utilizing variant residences together with the ratio of Red channel spectral from RGB, with a distinction in chromaticity color space, and average image depth is applied for self-shadow and forged shadow detection. A study on shadow discovery methods utilizing multi-light sources suggests upon a territory [11] when the mild from a supply can't reap the place due to obstacle via a query. The shadows play a critical role in giving essential data of an object of interest. In any case, they cause an issue in computer vision software, as an example, division, query region, and protest numbering. Accordingly, shadow reputation and evacuation are prepreparing undertakings in several computer vision packages. Multi-mild shadow identity method with expulsion techniques utilizing a mixture of tri-shading version, pressure version and LAB shading area for a discovery of various shadow has been done. Computer vision and image analysis application are affected by shadow [12] and it causes some unwanted changes in object extraction, segmentation and recognition because of the inappropriate
Kuldip Acharya et al. / Procedia Computer Science 132 (2018) 103–108 Kuldip Acharya et al. / Procedia Computer Science 00 (2018) 000–000
105
classification of shadow points as object points or foreground. So, shadow detection is a challenging task in computer vision application. Appropriate shadow detection is a difficult task due to the variation in light of the background and resemblance between the presence of the objects and background. Many algorithms and techniques have been introduced for the various natural situations to identify shadow from the images. 3. Methodology The angle of illumination for creating a shadow is not always a fixed one but it may be drifted due to the shifting the source of light, maybe Sunlight or any artificial source of light. These angles have a great impact on the shadow of the object. This angular variation is realized by an overlapping shadow of the same object and this shadowed area then becomes and overlapping hazy area. The uncertainty of the shape of the shadow creates some confusion during visualization. These effects can be utilized to measure the shadow accurately, especially in angular direction and to measure the time of the day very accurately by using any arbitrary object.
r
R G arctan RG 4
(1)
The converse of the tangent function gives the arctan function. It gives the angle whose tangent is a conferred digit. Here, R denotes red color, G denotes green color. In Taylor’s Theorem, if f is a continuous function and it is differentiable for n times which interval is [x, x + h], then some point exists there in this interval, which denoted by x + λh for some λ ∈ [0, 1], such that
f ( x h ) f ( x) hf ' ( x)
h2 '' h( n1) ( n 1) hn n f ( x) ... f f ( x h) ( x) n! 2 (n 1)!
(2)
Here, the angular resolution of the shadow depends on the direction of the source of the light. A slight change of direction of light source creates the change of angular shadow of the object. The shadow is a function of the directive angle of the light source. This function changes when the angle changes. x = theta and h= delta. Here, theta= 0.3 If f is a well-known analytic function where remains the derivatives of all orders, then one may think to grow the value of n indefinitely. Due to the correlation between red (R) and green (G) components and with the inherent correlation between the energy part and color component (RGB) in RGB color space, the formula may be rewritten as: 3.1. Proposed method
where,
p
p
4
R G tan 1 p RG
(3)
is the angle of ambiguity due to the above-mentioned correlation.
4. Results and Analysis The shadow of a human being is generated by animated video and it can be annotated a real shadow. The shadow is generated by a single source of light as shown in the frames of video frames. It is seen that the location of the shadow depends on the location of the source of light. In the animated video frame, more than one light sources are 3
Kuldip Acharya et al. / Procedia Computer Science 132 (2018) 103–108 Kuldip Acharya et al. / Procedia Computer Science 00 (2018) 000–000
106
used and it is observed more than one is created and they are seen to be overlapped. When the source of light is moved by a very small angle the shadows are observed to be shifted in angular directions. From two consecutive such image frames, the angular shift in the location of the shadows can be calculated by using Euclidian geometry. The shift in angle may be used to find the angular movement of the lights also. In normal daylight, the length, breadth of the shadow depends on the time of the day. Hence the shifting angle can be used to measure the timing of the day. The overlap angle can also be utilized to confuse and disorient the enemies’ bomber airplanes and this concept is valid for any object to consider as a target.
(a)
(b)
(a)
(c)
(e)
(d)
(f)
Fig. 1. (a) (b) Original video frames; (c) (d) Shadows of detected objects are shown by shadow boundaries; (e) Shadow mask; (f) Shadow ratio, colormap, and color bar.
Kuldip Acharya et al. / Procedia Computer Science 00 (2018) 000–000
Kuldip Acharya et al. / Procedia Computer Science 132 (2018) 103–108
(a)
(c)
107
(b)
(d)
Fig. 2. (a) (c) Original color invariants method [1]; (b) (d) Propose modified method gives more prominent results.
5. Conclusion and Future Work Detection of a shadow of animated video frames in RGB color space effect of the single and multiple light source on the formation of the shadow of an object has been studied. The results obtained are related to the benefit of the mankind in terms of the safety measure from the enemies and to find out the daytime found in an animated video environment. The Taylor series expansion and concept of offset angle arising out of the correlation between RGB component has also been taken account in the present study to detect the shadows of the moving objects in consecutive animated video frames. The proposed algorithm applied to three-dimensional animated images and simulated in Matlab software. The simulation results show that the methods propose modified method gives more prominent results than original color invariants method to detect the shadows. In future, fuzzy adaptive measures for real-time shadow detection from animated videos will be considered for more accurate results.
5
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