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.

Learning Pedestrian Trajectories with Kernels

Ricci, Elisa;Tobia, Francesco;
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 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/10968
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