Color and Shape Based Automatic Detection of Pedestrians in Surveillance Videos

Harihara Santosh Dadi*, Gopala Krishna Mohan Pillutla **, Madhavi Latha Makkena ***
* Associate Professor, Department of Electronics and Communication Engineering, Aditya Institute of Technology and Management, Hyderabad, Andhra Pradesh, India.
** Professor, Department of Electronics and Communication Engineering, Institute of Aeronautical College of Engineering, Hyderabad, Andhra Pradesh, India.
*** Professor, Department of Electronics and Communication Engineering, JNT University, Telangana, India.
Periodicity:January - March'2018
DOI : https://doi.org/10.26634/jip.5.1.14612

Abstract

Detection of human is the first and foremost step in any human tracking system. Color and shape based automated human detection algorithm in surveillance videos have been proposed in this paper. Every frame is first divided into R, G, and B frames. The background images for these three basic colors are formed, and three binary images for three colors are formed for every frame. One binary image overlaps over the other to find the common rectangles. Based on the aspect ratio of the rectangle, humans are detected. The proposed algorithm uses both color and the shape aspect in finding the humans. The performance results show that the proposed algorithm has good metrics.

Keywords

Automated Human Detection, Binary Images, Surveillance Videos, Color Images.

How to Cite this Article?

Dadi, H.S., Pillutla, G.K.M. and Makkena, M.L. (2018). Color and Shape Based Automatic Detection of Pedestrians in Surveillance Videos. i-manager’s Journal on Image Processing, 5(1), 1-11. https://doi.org/10.26634/jip.5.1.14612

References

[1]. Beleznai, C., Fruhstuck, B., & Bischof, H. (2004). Human detection in groups using a fast mean shift procedure. In Image Processing, 2004. ICIP'04. 2004 International Conference on (Vol. 1, pp. 349-352). IEEE.
[2]. Cutler, R., & Davis, L. S. (2000). Robust real-time periodic motion detection, analysis, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 781-796.
[3]. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp. 886-893). IEEE.
[4]. Elzein, H., Lakshmanan, S., & Watta, P. (2003). A motion and shape-based pedestrian detection algorithm. In Intelligent Vehicles Symposium, 2003. Proceedings. IEEE (pp. 500-504). IEEE.
[5]. Eng, H. L., Wang, J., Kam, A. H., & Yau, W. Y. (2004). A bayesian framework for robust human detection and occlusion handling human shape model. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17 International Conference on (Vol. 2, pp. 257-260). IEEE.
[6]. Gavrila, D. M., & Giebel, J. (2002). Shape-based pedestrian detection and tracking. In Intelligent Vehicle Symposium, 2002. IEEE (Vol. 1, pp. 8-14). IEEE.
[7]. Haga, T., Sumi, K., & Yagi, Y. (2004). Human detection in outdoor scene using spatio-temporal motion analysis. In Pattern Recognition, 2004. ICPR 2004. Proceedings of th the 17 International Conference on (Vol. 4, pp. 331-334). IEEE.
[8]. Han, J., & Bhanu, B. (2003). Detecting moving humans using color and infrared video. In Multisensor Fusion and Integration for Intelligent Systems, MFI2003. Proceedings of IEEE International Conference on (pp. 228-233). IEEE.
[9]. Jiang, L., Tian, F., Shen, L. E., Wu, S., Yao, S., Lu, Z., & Xu, L. (2004, October). Perceptual-based fusion of ir and visual images for human detection. In Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on (pp. 514-517). IEEE.
[10]. Lee, D. J., Zhan, P., Thomas, A., & Schoenberger, R. B. (2004). Shape-based human detection for threat assessment. In Visual Information Processing XIII (Vol. 5438, pp. 81-92). International Society for Optics and Photonics.
[11]. Li, L., Ge, S. S., Sim, T., Koh, Y. T., & Hunag, X. (2004). Object-oriented scale-adaptive filtering for human detection from stereo images. In Cybernetics and Intelligent Systems, 2004 IEEE Conference on (Vol. 1, pp. 135-140). IEEE.
[12]. Ogale, N. A. (2006). A survey of techniques for human detection from video. Survey, University of Maryland, 125(133), 19.
[13]. Sidenbladh, H. (2004). Detecting human motion with support vector machines. In Pattern Recognition, th 2004. ICPR 2004. Proceedings of the 17 International Conference on (Vol. 2, pp. 188-191). IEEE.
[14]. Toth, D., & Aach, T. (2003). Detection and recognition of moving objects using statistical motion detection and Fourier descriptors. In Image Analysis and th Processing, 2003. Proceedings. 12 International Conference on (pp. 430-435). IEEE.
[15]. Utsumi, A., & Tetsutani, N. (2002). Human detection using geometrical pixel value structures. In Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on (pp. 39-44). IEEE.
[16]. Viola, P., Jones, M. J., & Snow, D. (2003). Detecting pedestrians using patterns of motion and appearance. In null (p. 734-741). IEEE.
[17]. Wren, C. R., Azarbayejani, A., Darrell, T., & Pentland, A. P. (1997). Pfinder: Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 780-785.
[18]. Xu, F., & Fujimura, K. (2003). Human detection using depth and gray images. In Advanced Video and Signal Based Surveillance, 2003. Proceedings. IEEE Conference on (pp. 115-121). IEEE.
[19]. Yoon, S. M., & Kim, H. (2004). Real-time multiple people detection using skin color, motion and appearance information. In Robot and Human th Interactive Communication, 2004. ROMAN 2004. 13 IEEE International Workshop on (pp. 331-334). IEEE.
[20]. Zhou, J., & Hoang, J. (2005). Real time robust human detection and tracking system. In Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on (pp. 149-149). IEEE.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.