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

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.</p

Lightweight Learning for Partial and Occluded Person Re-identification

Occluded and partial person re-identification (re-ID) problems have emerged as challenging research topics in the area of computer vision. Existing part-based models, with complex designs, fail to properly tackle these problems. The reasons for their failures are two-fold. Firstly, individual body part appearances are not discriminative enough to distinguish between two closely appearing persons. Secondly, re-identification datasets typically lack detailed human body-part annotations. To address these challenges, we present a lightweight yet accurate solution for partial person re-identification. Our proposed approach consists of two key components, namely, design of a lightweight Unary-Binary projective Dictionary Learning (UBDL) model, and, construction of a similarity matrix for distilling knowledge from the deep Omni-scale network (OSNet) to UBDL. The unary dictionary (UD) pair encodes patches horizontally, ignoring the viewpoints. The binary dictionary (BD) pairs, on the other hand, are learned between two views, giving more weight to less occluded vertical patches for improving the correspondence across the views. We formulate appropriate convex objective functions for unary and binary cases by incorporating the above knowledge similarity matrix. Closed-form solutions are obtained for updating unary and binary dictionary components. Final matching scores are computed by fusing unary and binary matching scores with adaptive weighting of relevant cross-view patches. Extensive experiments and ablation studies on a number of occluded and partial re-identification datasets like Occluded-REID (O-REID), Partial-REID (PREID) and Partial-iLIDS (P-iLIDS), clearly showcase the merits of our proposed solution.</p