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4 March 2015 Person identification from streaming surveillance video using mid-level features from joint action-pose distribution
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Proceedings Volume 9407, Video Surveillance and Transportation Imaging Applications 2015; 94070P (2015) https://doi.org/10.1117/12.2083423
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
Abstract
We propose a real time person identification algorithm for surveillance based scenarios from low-resolution streaming video, based on mid-level features extracted from the joint distribution of various types of human actions and human poses. The proposed algorithm uses the combination of an auto-encoder based action association framework which produces per-frame probability estimates of the action being performed, and a pose recognition framework which gives per-frame body part locations. The main focus in this manuscript is to effectively com- bine these per-frame action probability estimates and pose trajectories from a short temporal window to obtain mid-level features. We demonstrate that these mid-level features captures the variation in the action performed with respect to an individual and can be used to distinguish one person from the next. Preliminary analysis on the KTH action dataset where each sequence is annotated with a specific person and a specific action is provided and shows some interesting results which verify this concept.
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Binu M. Nair and Vijayan K. Asari "Person identification from streaming surveillance video using mid-level features from joint action-pose distribution", Proc. SPIE 9407, Video Surveillance and Transportation Imaging Applications 2015, 94070P (4 March 2015); https://doi.org/10.1117/12.2083423
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