This project implements a novel architecture that combines Change Detection, Anomaly Detection, Clustering and Video Summarization for effectively summarizing lengthy CCTV footage around crime anomalies. Dataset: UCF-crime dataset
Explainable video anomaly detection that fuses CLIP visual features with BLIP captions through bidirectional cross-modal attention, plus a temporal branch that cuts false alarms by 46%. IEEE IConSCEPT 2025.
DA-ZVAD: video anomaly detection that adapts to a new camera from one sentence, with no training. Frozen CLIP + LLaVA, and the system writes the sentence itself. 0.824 AUROC on UCF-Crime, above published training-free methods. M.Tech research, NIT Calicut.
AI-powered real-time video anomaly detection using CNN + LSTM, trained on UCF Crime Dataset
Event detection and zero-shot spatial localization with V-JEPA 2.1, PEFT, Integrated Grandients and VTCD
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