Crowd video retrieval via deep attribute-embedding graph ranking

Since the number of surveillance cameras in public areas increases very fast, massive crowd videos are captured and shared, which brings an urgent need to retrieve these videos efficiently and effectively. However, most recent research on crowd video mainly focused on crowd behavior understanding and abnormal detection. In this study, as the very first attempt, we propose a crowd video retrieval method via deep attribute-embedding graph ranking. Group profiling attributes are capable of reflecting rich crowd patterns in videos. To deeply embed the specific relationship and manifold structure of crowd patterns, we integrate graph ranking, optimized weights learning and deep metric transforming in a unified regularization framework for crowd video retrieval. To sufficiently explore the effects of multiple attributes and their complementation in crowds, we devise several scene-independent visual descriptors specifically for each crowd video. Interpretable descriptors are categorized into different levels and structures as group profiling attributes, according to semantic properties of crowd patterns. Extensive experiments conducted on CUHK crowd dataset demonstrate the effectiveness and superiority of the proposed approach.