Fine-grained temporal-spatial cues for theft recognition in surveillance videos.

Surveillance systems play a crucial role in detecting suspicious human activities, including attacks, violence, and abductions, in public spaces. This study presents a human intervention-free, hybrid framework that utilizes deep neural networks for real-time theft activity recognition. The proposed methodology employs a dual stream fusion network, combining appearance and motion features, to accurately identify theft actions. Specifically, a modified InceptionV3 model extracts relevant body pose features through keypoint transfer, feeding two separate deep neural network pipelines for appearance and motion analysis. Long-Short-Term Memory network then models temporal relationships between the extracted features across consecutive frames. The novelty of this research lies in the proposed dual-stream fusion architecture, which aims to capture fine-grained temporal and spatial cues for theft detection. A new lab-lifting dataset has also been developed to reflect subtle theft behaviors in academic settings. The framework's performance is evaluated on a dataset comprising normal and theft activities. The results demonstrate a recognition accuracy of 91.86% , surpassing that of other methods.