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 Ker- nel K-means with a Dynamic Time Warping (DTW) ker- nel. 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.
Learning Pedestrian Trajectories with Kernels
RICCI, ELISA;
2010
Abstract
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 Ker- nel K-means with a Dynamic Time Warping (DTW) ker- nel. 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.File in questo prodotto:
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