Deep Guard: A Comprehensive Deep Learning System for Unmasking Suspicious Activities in Surveillance Footage - Piyush Jain; Shreyash Chole; Rishabh Nath Tiwari; Samiullah Siddiqui (International Journal of Satellite Remote Sensing, 2023)
These days, video surveillance is quite vital. Technology has evolved considerably as machine learning, artificial intelligence, and deep learning become increasingly widespread. There are several algorithms that assist in identifying distinct kinds of suspicious behaviour from live footage by combining the techniques. A person's behaviour is the most unpredictable thing, and it can be quite challenging to determine whether it is normal or suspicious. Video surveillance is automated to address this. On CCTV cameras, it is currently not feasible to manually watch every incident. It is a waste of time to manually look for the identical occurrence in the recorded video, even if it has already occurred. An emerging area in automated video surveillance systems is the analysis of anomalous events in video.To identify questionable or odd behaviour in the academic setting and notify the relevant authorities if any suspicious conduct is found, a deep learning technique is employed. For surveillance purposes, a collection of still photos from a video is commonly employed. Each frame is divided into two halves. In the first phase, the features are computed from the video frames; in the second, the classifier uses the retrieved features to determine whether the class is normal or suspicious.
Keywords: Deep Learning; Electronic article surveillance (EAS); GPU (Graphic Processing Unit); Suspicious Activity; Video Surveillance