PHACT: parallel HOG and correlation tracking

Hassan, Waqas, Birch, Philip, Young, Rupert and Chatwin, Chris (2014) PHACT: parallel HOG and correlation tracking. Published in: Loce, Robert P and Saber, Eli, (eds.) Proceedings of IS&T / SPIE on Electronic Imaging: Video Surveillance and Transportation Imaging Applications; San Francisco, California, USA; 3-5 February 2014. 9026 2. Society of Photo Optical Instrumentation Engineers / Society for Imaging Science and Technology ISSN 0277-786X ISBN 9780819499431

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Abstract

Histogram of Oriented Gradients (HOG) based methods for the detection of humans have become one of the most reliable methods of detecting pedestrians with a single passive imaging camera. However, they are not 100 percent reliable. This paper presents an improved tracker for the monitoring of pedestrians within images. The Parallel HOG and Correlation Tracking (PHACT) algorithm utilises self learning to overcome the drifting problem. A detection algorithm that utilises HOG features runs in parallel to an adaptive and stateful correlator. The combination of both acting in a cascade provides a much more robust tracker than the two components separately could produce. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Item Type: Conference Proceedings
Additional Information: Copyright (2014) Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Research Centres and Groups: Industrial Informatics and Signal Processing Research Group
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Depositing User: Philip Birch
Date Deposited: 11 Nov 2014 12:37
Last Modified: 11 Jul 2017 09:37
URI: http://sro.sussex.ac.uk/id/eprint/51332

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