An adaptive training-less framework for anomaly detection in crowd scenes

Abstract

Anomaly detection in crowd videos has become a popular area of research for the computer vision community. Several existing methods have determined anomaly as a deviation from scene normalcy learned via separate training with/without labeled information. However, owing to rare and sparse nature of anomalous events, any such learning can be misleading as there exist no hardcore segregation between anomalous and non-anomalous events. To address such challenge, we propose an adaptive training-less system capable of detecting anomaly on-the-fly. Our solution pipeline consists of three major components, namely, adaptive 3D-DCT model for multi-object detection-based association, local motion descriptor generation through an improved saliency guided optical flow, and anomaly detection based on Earth mover's distance (EMD). The proposed model, despite being training-free, is found to achieve comparable performance with several state-of-the-art methods on publicly available UCSD, UMN, CUHK-Avenue and ShanghaiTech datasets.