Object detection in surveillance video from dense trajectories

Detecting objects such as humans or vehicles is a central problem in video surveillance. Myriad standard approaches exist for this problem. At their core, approaches consider either the appearance of people, patterns of their motion, or differences from the background. In this paper we build on dense trajectories, a state-of-the-art approach for describing spatio-temporal patterns in video sequences. We demonstrate an application of dense trajectories to object detection in surveillance video, showing that they can be used to both regress estimates of object locations and accurately classify objects.