Watch Where You’re Going! - Pedestrian Tracking Via Head Pose

Sankha S. Mukherjee, Rolf H. Baxter, Neil M. Robertson

2016

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.

Paper Citation

in Harvard Style

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 - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 573-579. DOI: 10.5220/0005786905730579

in Bibtex Style

@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 - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={573-579},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005786905730579},
isbn={978-989-758-175-5},
}

in EndNote Style

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