Action-centric Polar Representation of Motion Trajectories for Online Action Recognition

Abstract

This work introduces a novel action descriptor that represents activities instantaneously in each frame of a video sequence for action recognition. The proposed approach first characterizes the video by computing kinematic primitives along trajectories obtained by semi-dense point tracking in the video. Then, a frame level characterization is achieved by computing a spatial action-centric polar representation from the computed tra-jectories. This representation aims at quantifying the image space and grouping the trajectories within radial and angular regions. Motion histograms are then temporally aggregated in each region to form a kinematic signature from the current trajectories. Histograms with several time depths can be computed to obtain different motion characterization versions. These motion histograms are updated at each time, to reflect the kinematic trend of trajectories in each region. The action descriptor is then defined as the collection of motion histograms from all the regions in a specific frame. Classic support vector machine (SVM) models are used to carry out the classification according to each time depth. The proposed approach is easy to implement, very fast and the representation is consistent to code a broad variety of actions thanks to a multi-level representation of motion primitives. The proposed approach was evaluated on different public action datasets showing competitive results (94% and 88.7% of accuracy are achieved in KTH and UT datasets, respectively), and an efficient computation time.