Extracting Pathlets FromWeak Tracking Data

We present a novel framework for extracting "pathlets" from tracking data. A pathlet is defined as a motion region that contains tracks having the same origin and destination in the scene and that are temporally correlated. The proposed method requires only weak tracking data (multiple fragmented tracks per target). We employ a probabilistic state space representation to construct a Markovian transition model and estimate the scene entry/exit locations. The resulting model is treated as a set of vertices in a graph and a similarity matrix is built which describes broader nonlocal relationships between states. A Spectral Clustering approach is then used to automatically extract the pathlets of the scene. We present experimental results from scenes of varying difficulty and compare against other approaches.