Abstract
Visual Attention algorithms have been extensively used for object detection in images. However, the use of these algorithms for video analysis has been less explored. Many of the techniques proposed, though accurate and robust, still require a huge amount of time for processing large sized video data. Thus this paper introduces a fast and computationally inexpensive technique for detecting regions corresponding to moving humans in surveillance videos. It is based on the dynamic saliency model and is robust to noise and illumination variation. Results indicate successful extraction of moving human regions with minimum noise, and faster performance in comparison to other models. The model works best in sparsely crowded scenarios.
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References
Dick, A., Brooks, M.: Issues in Automated Visual Surveillance. In: Proceedings of International Conference on Digital Image Computing: Techniques and Application, pp. 195–204 (2003)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis (1998)
Frintrop, S.: VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search. LNCS (LNAI), vol. 3899. Springer, Heidelberg (2006)
Amudha, J., Soman, K.P., Padmakar Reddy, S.: A Knowledge Driven Computational Visual Attention Model. International Journal of Computer Science Issues 8(3(1)) (2011)
Radha, D., Amudha, J., Ramyasree, P., Ravindran, R., Shalini, S.: Detection of Unauthorized Human Entity in Surveillance Video. International Journal of Engineering and Technology 5(3) (2013)
Amudha, J., Mathur, P.: Keyframe Identification using Visual Attention Model. In: International Conference on Recent Trends in Computer Science and Engineering, Chennai, pp. 55–55 (2012)
Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on theory for warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)
Guo, W., Xu, C., Ma, S., Xu, M.: Visual Attention Based Motion Object Detection and Trajectory Tracking. In: Qiu, G., Lam, K.M., Kiya, H., Xue, X.-Y., Kuo, C.-C.J., Lew, M.S. (eds.) PCM 2010, Part II. LNCS, vol. 6298, pp. 462–470. Springer, Heidelberg (2010)
Zhang, S., Stentiford, F.: A saliency based object tracking method. In: International Workshop on Content-Based Multimedia Indexing, pp. 512–517 (2008)
Riche, N., Mancas, M., Culibrk, D., Crnojevic, V., Gosselin, B., Dutoit, T.: Dynamic saliency models and human attention: a comparative study on videos. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part III. LNCS, vol. 7726, pp. 586–598. Springer, Heidelberg (2013)
Mancas, M., Mancas-Thillou, C., Gosselin, B., Macq, B.: A Rarity-Based Visual Attention Map - Application to Texture Description. In: IEEE International Conference on Image Processing, pp. 445–448 (2006)
Performance Evaluation and Tracking and Surveillance, PETS (2001), http://ftp.pets.rdg.ac.uk/PETS2001/DATASET1/
Basharat, A., Gritai, A., Shah, M.: Learning object motion patterns for anomaly detection and improved object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly Detection in Crowded Scenes. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA (2010)
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Sanjay, G., Amudha, J., Jose, J.T. (2015). Moving Human Detection in Video Using Dynamic Visual Attention Model. In: El-Alfy, ES., Thampi, S., Takagi, H., Piramuthu, S., Hanne, T. (eds) Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-11218-3_12
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DOI: https://doi.org/10.1007/978-3-319-11218-3_12
Publisher Name: Springer, Cham
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