Exploring Depth Information for Head Detection with Depth Images

Siyuan Chen 1 Francois Bremond 1 Hung Nguyen 1 Hugues Thomas 1
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Head detection may be more demanding than face recognition and pedestrian detection in the scenarios where a face turns away or body parts are occluded in the view of a sensor, but locating people is needed. In this paper, we introduce an efficient head detection approach for single depth images at low computational expense. First, a novel head descriptor is developed and used to classify pixels as head or non-head. We use depth values to guide each window size, to eliminate false positives of head centers, and to cluster head pixels, which significantly reduce the computation costs of searching for appropriate parameters. High head detection performance was achieved in experiments – 90% accuracy for our dataset containing heads with different body postures, head poses, and distances to a Kinect2 sensor, and above 70% precision on a public dataset composed of a few daily activities, which is higher than using a head-shoulder detector with HOG feature for depth images.
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Communication dans un congrès
AVSS 2016 - 13th International Conference on Advanced Video and Signal-Based Surveillance, Aug 2016, Colorado Springs, United States. AVSS 2016
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Siyuan Chen, Francois Bremond, Hung Nguyen, Hugues Thomas. Exploring Depth Information for Head Detection with Depth Images. AVSS 2016 - 13th International Conference on Advanced Video and Signal-Based Surveillance, Aug 2016, Colorado Springs, United States. AVSS 2016. 〈hal-01414757〉

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