High Density Crowd Behaviors Recognition Based on Micro-behavioral and Sparse Representation

Abstract

This paper presents a novel method to recognize high density crowd behaviors using micro-behaviors combining with Sparse Representation based on Locally Linear Embedding (named LLE-based Sparse Representation or LLE-SR). We extract micro-behaviors from each frame, respectively named Fountainhead, Bottleneck, Blocking, Lane and Ring/Arch, and construct micro-behaviors histograms to better describe complex high density crowd scenes. As the mid-lever semantic, micro-behaviors solve the gap between the high-level semantics and low-level semantic, and the creation of them does not need any information of target track. LLE-SR method fully considers the behavior of local manifold structure of samples. Through adding LLE regularization term in sparse classification model, the unstable property of the manifold structure can be solved, and then recognition rate is improved. Numerous experiments have been conducted in real scenes, the results of which demonstrate the effectiveness and robustness of the proposed method for high density crowd behavior recognition.