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Anomaly detection in crowd videos has become a popular area of research forthe computer vision community. Several existing methods generally perform aprior training about the scene with or without the use of labeled data.However, it is difficult to always guarantee the availability of prior data,especially, for scenarios like remote area surveillance. To address suchchallenge, we propose an adaptive training-less system capable of detectinganomaly on-the-fly while dynamically estimating and adjusting response based oncertain parameters. This makes our system both training-less and adaptive innature. Our pipeline consists of three main components, namely, adaptive 3D-DCTmodel for multi-object detection-based association, local motion structuredescription through saliency modulated optic flow, and anomaly detection basedon earth movers distance (EMD). The proposed model, despite beingtraining-free, is found to achieve comparable performance with severalstate-of-the-art methods on the publicly available UCSD, UMN, CHUK-Avenue andShanghaiTech datasets.

An Adaptive Training-less System for Anomaly Detection in Crowd Scenes