Learning Pedestrian Trajectories with Kernels

We present a novel method for learning pedestrian trajectories which is able to describe complex motion patterns such as multiple crossing paths. This approach adopts Kernel Canonical Correlation Analysis (KCCA) to build a mapping between the physical location space and the trajectory patterns space. To model crossing paths we rely on a clustering algorithm based on Kernel K-means with a Dynamic Time Warping (DTW) kernel. We demonstrate the effectiveness of our method incorporating the learned motion model into a multi-person tracking algorithm and testing it on several video surveillance sequences.