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Authors: Sankha S. Mukherjee ; Rolf H. Baxter and Neil M. Robertson

Affiliation: Heriot-Watt University, United Kingdom

Keyword(s): Deep Learning, Intentional Tracker.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Motion, Tracking and Stereo Vision ; Tracking and Visual Navigation ; Video Surveillance and Event Detection

Abstract: In this paper we improve pedestrian tracking using robust, real-time human head pose estimation in low resolution RGB data without any smoothing motion priors such as direction of motion. This paper presents four principal novelties. First, we train a deep convolutional neural network (CNN) for head pose classification with data from various sources ranging from high to low resolution. Second, this classification network is then fine-tuned on the continuous head pose manifold for regression based on a subset of the data. Third, we attain state-of-art performance on public low resolution surveillance datasets. Finally, we present improved tracking results using a Kalman filter based intentional tracker. The tracker fuses the instantaneous head pose information in the motion model to improve tracking based on predicted future location. Our implementation computes head pose for a head image in 1.2 milliseconds on commercial hardware, making it real-time and highly scalable.

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Paper citation in several formats:
Mukherjee, S.; Baxter, R. and Robertson, N. (2016). Watch Where You’re Going! - Pedestrian Tracking Via Head Pose. In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 3: VISAPP; ISBN 978-989-758-175-5; ISSN 2184-4321, SciTePress, pages 573-579. DOI: 10.5220/0005786905730579

@conference{visapp16,
author={Sankha S. Mukherjee. and Rolf H. Baxter. and Neil M. Robertson.},
title={Watch Where You’re Going! - Pedestrian Tracking Via Head Pose},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 3: VISAPP},
year={2016},
pages={573-579},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005786905730579},
isbn={978-989-758-175-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 3: VISAPP
TI - Watch Where You’re Going! - Pedestrian Tracking Via Head Pose
SN - 978-989-758-175-5
IS - 2184-4321
AU - Mukherjee, S.
AU - Baxter, R.
AU - Robertson, N.
PY - 2016
SP - 573
EP - 579
DO - 10.5220/0005786905730579
PB - SciTePress