Head pose estimation with neural networks from surveillant images

Yichao Cai; Xiao Zhou; Dachuan Li; Yifei Ming; Xingang Mou

doi:10.1117/12.2523090

15 March 2019 Head pose estimation with neural networks from surveillant images

Yichao Cai, Xiao Zhou, Dachuan Li, Yifei Ming, Xingang Mou

Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 1104112 (2019) https://doi.org/10.1117/12.2523090
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany

Abstract

Estimating head pose of pedestrians is a crucial task in autonomous driving system. It plays a significant role in many research fields, such as pedestrian intention judgment and human-vehicle interaction, etc. While most of the current studies focus on driver’s-view images, we reckon that surveillant images are also worthy of attention since more global information can be obtained from them than driver’s-view images. In this paper, we propose a method for head pose estimation from surveillant images. This approach consists of two stages, head detection and pose estimation. Since the head of pedestrian takes up a very small number of pixels in a surveillant image, a two-step strategy is used to improve the performance in head detection. Firstly, we train a model to extract body region from the source image. Secondly, a head detector is trained to locate head position from the extracted body regions. We use YOLOv3 as our detection network for both body and head detection. For head pose estimation, we treat it as classification task of 10 categories. We use ResNet-50 as the backbone of the classifier, of which the input is the result of head detection. A serial of experiments demonstrate the good performance of our proposed method.

Citation Download Citation

Yichao Cai, Xiao Zhou, Dachuan Li, Yifei Ming, and Xingang Mou "Head pose estimation with neural networks from surveillant images", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 1104112 (15 March 2019); https://doi.org/10.1117/12.2523090

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