Dynamic View Selection for Human Motion Analysis in Camera Networks
1 online resource (111 pages) : PDF
University of North Carolina at Charlotte
Automated human activity analysis for multi-camera networks requires algorithms that are both accurate and efficient for practical, real-time use. Current approaches face a trade-off between accuracy and speed, with the most accurate methods having high computational cost. This work is motivated by the observation that multi-camera networks provide redundant data across views. In many cases, algorithms could perform accurate analysis with a subset of the available information. We propose algorithms that dynamically select a subset of the network cameras for each stage of the automated analysis pipeline. The goal of this research is to develop algorithms for multi-camera networks with high computational efficiency without sacrificing accuracy. In particular, we focus on solving core computer vision problems related to the human motion analysis pipeline: detection, tracking, pose estimation, and action recognition. Experiments on benchmark datasets demonstrate the applicability of dynamic view selection to each of these areas.
ACTION RECOGNITIONCOMPUTER VISIONDETECTIONHEAD POSE ESTIMATIONTRACKING
Shin, MinZhang, ShaotingXiao, JingSauda, Eric
Thesis (Ph.D.)--University of North Carolina at Charlotte, 2015.
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