Action Detection in Crowd

For automated surveillance, it is useful to detect specific actions performed by people in busy natural environments. This differs from and thus more challenging than the intensively studied action recognition problem in that for action detection in crowd an action of interest is often overwhelmed by large number of background activities of other objects in the scene. Motivated by the success of sliding-window based 2D object detection approaches, in this paper, we propose to tackle the problem by learning a discriminative classifier from annotated 3D action cuboids to capture the intra-class variation, and sliding 3D search windows for detection. The key innovation of our method is a novel greedy k nearest neighbour algorithm for automated annotation of positive training data, by which an action detector can be learned with only a single training sequence being annotated thus greatly alleviating the tedious and unreliable 3D manual annotation. Extensive experiments on real-world action detection datasets demonstrate that our detector trained with minimal annotation can achieve comparable results to that learned with full annotation, and outperforms existing methods.