Scene understanding by statistical modeling of motion patterns

We present a novel method for the discovery and statistical representation of motion patterns in a scene observed by a static camera. Related methods involving learning of patterns of activity rely on trajectories obtained from object detection and tracking systems, which are unreliable in complex scenes of crowded motion. We propose a mixture model representation of salient patterns of optical flow, and present an algorithm for learning these patterns from dense optical flow in a hierarchical, unsupervised fashion. Using low level cues of noisy optical flow, K-means is employed to initialize a Gaussian mixture model for temporally segmented clips of video. The components of this mixture are then filtered and instances of motion patterns are computed using a simple motion model, by linking components across space and time. Motion patterns are then initialized and membership of instances in different motion patterns is established by using KL divergence between mixture distribution...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Imran Saleemi, Lance Hartung, Mubarak Shah
• Keywords: Computer Vision, CVPR 2010, Mixture Model, Motion Patterns, Optical flow