International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Real-Time Suspicious Activity Detection in Surveillance Video |
|---|---|
| ABSTRACT | The rapid expansion of surveillance camera networks has vastly outpaced the hu- man capacity to effectively monitor them, creating a critical bottleneck in proac- tive security. While automated anomaly detection offers a potential solution, existing models frequently struggle to balance the high computational speed re- quired for real-time processing with the precision needed to minimize false alarms. To address these limitations, this paper presents a robust, automated framework for real-time suspicious activity detection in continuous surveillance video streams. Our approach leverages a spatiotemporal deep learning architecture—specifically utilizing a CNN-LSTM network—to simultaneously analyze spatial features and temporal movement patterns. By establishing a baseline of normative behavior within a given environment, the system autonomously identifies and flags devia- tions indicative of threats, such as physical altercations, loitering, or unauthorized access. A key focus of this research is computational optimization, ensuring the model operates with low latency for immediate alerting without requiring pro- hibitive hardware resources. We evaluated the proposed framework on the [Insert Dataset, e.g., UCF-Crime] dataset, where it achieved an accuracy of [Insert %] and significantly reduced false-positive rates compared to baseline models. The results demonstrate that our system is highly viable for real-world deployment, successfully bridging the gap between high-accuracy behavioral analysis and the strict time constraints of active security monitoring. |
| AUTHOR | SWAMINI RAJENDRA DESHMANE, PROF. DR. S. A. ITKAR M.Tech Student, Department of Computer Engineering, P.E.S. Modern College of Engineering, Affiliated to Savitribai Phule Pune University (SPPU), Pune, India Head of Department, Department of Computer Engineering, P.E.S. Modern College of Engineering, Affiliated to Savitribai Phule Pune University (SPPU), Pune, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406011 |
| pdf/11_Real-Time Suspicious Activity Detection in Surveillance Video.pdf | |
| KEYWORDS | |
| References | [1] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788. [2] J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” in Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recogni- tion (CVPR), 2017, pp. 7263–7271. [3] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint arXiv:2004.10934, 2020. [4] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Com- putation, vol. 9, no. 8, pp. 1735–1780, 1997. [5] J. Donahue et al., “Long-term Recurrent Convolutional Networks for Vi- sual Recognition and Description,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 2625–2634. [6] K. Simonyan and A. Zisserman, “Two-Stream Convolutional Networks for Action Recognition in Videos,” in Advances in Neural Information Processing Systems (NIPS), 2014, pp. 568–576. [7] D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning Spatiotemporal Features with 3D Convolutional Networks,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 4489–4497. [8] A. Ullah, J. Ahmad, K. Muhammad, M. Sajjad, and S. W. Baik, “Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features,” IEEE Access, vol. 6, pp. 1155–1166, 2017. [9] W. Sultani, C. Chen, and M. Shah, “Real-world Anomaly Detection in Surveillance Videos,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 6479–6488. [10] W. Liu, W. Luo, D. Lian, and S. Gao, “Future Frame Prediction for Anomaly Detection – A New Baseline,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 6536–6545. [11] M. Hasan, J. Choi, J. Neumann, A. K. Roy-Chowdhury, and L. S. Davis, “Learning Temporal Regularity in Video Sequences,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 733–742. [12] J.-X. Zhong, N. Li, W. Kong, S. Liu, T. H. Li, and G. Li, “Graph Con- volutional Label Noise Cleaner: Train a Plug-and-Play Action Classifier for Anomaly Detection,” in Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), 2019, pp. 1237–1246. [13] B. Ramachandra, M. J. Jones, and R. R. Vatsavai, “A Survey of Single- Scene Video Anomaly Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 5, pp. 2293–2312, May 2022. [14] V. Mahadevan, W. Li, V. Bhalodia, and N. Vasconcelos, “Anomaly Detection in Crowded Scenes,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, pp. 1975–1981. [15] C. Lu, J. Shi, and J. Jia, “Abnormal Event Detection at 150 FPS in MAT- LAB,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013, pp. 2720–2727. |
