Robust unsupervised motion pattern inference from video and applications

Abstract

We propose an unsupervised learning framework to infer motion patterns in videos and in turn use them to improve tracking of moving objects in sequences from static cameras. Based on tracklets, we use a manifold learning method Tensor Voting to infer the local geometric structures in (x, y) space, and embed tracklet points into (x, y, θ) space, where θ represents motion direction. In this space, points automatically form intrinsic manifold structures, each of which corresponds to a motion pattern. To define each group, a novel robustmanifold grouping algorithm is proposed. Tensor Voting is performed to provide multiple geometric cues which formulate multiple similarity kernels between any pair of points, and a spectral clustering technique is used in this multiple kernel setting. The grouping algorithm achieves better performance than state-of-the-art methods in our applications. Extracted motion patterns can then be used as a prior to improve the performance of any object tracker. It is especially useful to reduce false alarms and ID switches. Experiments are performed on challenging real-world sequences, and a quantitative analysis of the results shows the framework effectively improves state-of-the-art tracker.