Suspicious Activity Detection using Image Processing
DOI:
https://doi.org/10.46243/jst.2021.v6.i04.pp114-119Keywords:
Deep learning, Video surveillanceAbstract
Nowadays, video surveillance is used almost everywhere for security. The traditional method of monitoring cameras requires constant human intervention. Using Deep Learning and Image Processing, the proposed work aims to eliminate time and effort wasted on monitoring video surveillance cameras. Predicting human behavior is almost impossible. Deep Learning is used to detect suspicious and non-suspicious activity and to warn the user if any suspicious activity is detected. The proposed system strives for the detection of real-world suspicious activities such as burglaries, assaults etc. in surveillance videos.
References
Nishani, E., & Çiço, B. (2017, June). Computer vision approaches based on deep learning and neural networks: Deep neural networks for video analysis of human pose estimation in 2017 6th Mediterranean Conference on Embedded Computing (MECO) (pp. 1-4) IEEE
Naimat Ullah Khan, Wagon Wan (2018, July) A Review of Human Pose Estimation from Single Image”- 978-1-5386-5195- 7/18/2018IEEE
Chen, Q., Zhang, C., Liu, W., & Wang, D. (2018, October). SHPD: Surveillance human pose dataset and performance evaluation for coarsegrained pose estimation in 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 4088-4092) IEEE
Ai, B., Zhou, Y., Yu, Y., & Du, S. (2017, March). Human pose estimation using deep structure guided learning in 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1224-1231) IEEE
Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017). Realtime multi-person 2d pose estimation using part affinity fields in proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7291-7299)
Kim, H., Lee, S., Lee, D., Choi, S., Ju, J., & Myung, H. (2015). Real-time human pose estimation and gesture recognition from depth images using super pixels and SVM classifier Sensors, 15(6), 12410-12427
Tripathi, R. K., Jalal, A. S., & Agrawal, S. C. (2018). Suspicious human activity recognition: a review. Artificial Intelligence Review, 50(2), 283-339
Eksioglu, E. M. (2016). Decoupled algorithm for MRI reconstruction using nonlocal block matching model: BM3D-MRI. Journal of Mathematical Imaging and Vision, 56(3), 430-440
Wang, S., Su, Z., Ying, L., Peng, X., Zhu, S., Liang, F.,... & Liang, D. (2016, April). Accelerating magnetic resonance imaging via deep learning in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) (pp. 514-517). IEEE
Xu, L., Ren, J. S., Liu, C., & Jia, J. (2014). Deep convolutional neural network for image deconvolution. advances in neural information processing systems, 27, 1790-1798
Yang, Y., Sun, J., Li, H., & Xu, Z. (2016, December). Deep ADMM-Net for compressive sensing MRI in proceedings of the 30th international conference on neural information processing systems (pp. 10-18)
Zhan, Z., Cai, J. F., Guo, D., Liu, Y., Chen, Z., & Qu, X. (2015). Fast multiclass dictionaries learning with geometrical directions in MRI reconstruction IEEE Transactions on biomedical engineering, 63(9), 1850-1861. P ublished by: Longman Publishers www.jst.org.in 119 | Page
