Local spatio-temporal feature based voting framework for complex human activity detection and localization

Complex human activity detection is a challenging problem, especially when people interact with each other. Approaches utilizing local spatio-temporal features work well with background clutter, scale and illumination changing. However, most of them focus on classifying short video sequences. In real world applications such as surveillance, it´s hard to get the well segmented video clip to classify. So how to detect and localize complex human activities in unsegmented videos is a problem need to be solved. In this paper, based on the local spatio-temporal feature, we propose a variation of Hough Voting method using the Implicit Shape Model which can localize and recognize complex human activity simultaneously. Our approach is tested on the UT-Interaction dataset, and demonstrates promising results in complex human activity detection and localization.