J
James Ferryman
Researcher at University of Reading
Publications - 119
Citations - 3650
James Ferryman is an academic researcher from University of Reading. The author has contributed to research in topics: Video tracking & Object detection. The author has an hindex of 28, co-authored 115 publications receiving 3274 citations. Previous affiliations of James Ferryman include Bosch & Washington University in St. Louis.
Papers
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Proceedings ArticleDOI
PETS 2016: Dataset and Challenge
TL;DR: The PETS 2016 workshop as mentioned in this paper addressed the application of on-board multi-sensor surveillance for protection of mobile critical assets, where sensors (visible and thermal cameras) are mounted on the asset itself and surveillance is performed around the asset.
Journal ArticleDOI
A survey of human motion analysis using depth imagery
TL;DR: This survey starts by explaining the advantages of depth imagery, then describes the new sensors that are available to obtain it, and describes the software libraries that can acquire it from a sensor.
Proceedings ArticleDOI
PETS2009: Dataset and challenge
James Ferryman,Ali Shahrokni +1 more
TL;DR: This paper describes the crowd image analysis challenge that forms part of the PETS 2009 workshop and uses new or existing systems for i) crowd count and density estimation, ii) tracking of individual(s) within a crowd, and iii) detection of separate flows and specific crowd events, in a real-world environment.
Proceedings ArticleDOI
PETS Metrics: On-Line Performance Evaluation Service
D.P. Young,James Ferryman +1 more
TL;DR: The service allows researchers to submit their algorithm results for evaluation against a set of applicable metrics and the results of the evaluation processes are publicly displayed allowing researchers to instantly view how their algorithm performs against previously submitted algorithms.
Proceedings ArticleDOI
Visual surveillance for moving vehicles
TL;DR: An overview is given of a vision system for locating, recognising and tracking multiple vehicles, using an image sequence taken by a single camera mounted on a moving vehicle, using a template correlation approach.