Exploiting Multiple Cameras for Environmental Pathlets

We present a novel multi-camera framework to extract reliable pathlets [1] from tracking data. The proposed approach weights tracks based on their spatial and orientation similarity to simultaneous tracks observed in other camera views. The weighted tracks are used to build a Markovian state space of the environment and Spectral Clustering is employed to extract pathlets from a state-wise similarity matrix. We present experimental results on five multi-camera datasets collected under varying weather conditions and compare with pathlets extracted from individual camera views and three other multi-camera algorithms.