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. © 2010 Springer-Verlag.
CITATION STYLE
Streib, K., & Davis, J. W. (2010). Exploiting multiple cameras for environmental pathlets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6455 LNCS, pp. 613–624). https://doi.org/10.1007/978-3-642-17277-9_63
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