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Proceedings Paper

A biological cortex like target recognition and tracking in cluttered background
Author(s): Aristeidis Tsitiridis; Peter Yuen; Kan Hong; Tong Chen; Firmin Kam; James Jackman; David James; Mark Richardson
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Paper Abstract

This paper reports how objects in street scenes, such as pedestrians and cars, can be spotted, recognised and then subsequently tracked in cluttered background using a cortex like vision approach. Unlike the conventional pixel based machine vision, tracking is achieved by recognition of the target implemented in neuromorphic ways. In this preliminary study the region of interest (ROI) of the image is spotted according to the salience and relevance of the scene and subsequently target recognition and tracking of the object in the ROI have been performed using a mixture of feed forward cortex like neuromorphic algorithms together with statistical classifier & tracker. Object recognitions for four categories (bike, people, car & background) using only one set of ventral visual like features have achieved a max of ~70% accuracy and the present system is quite effective for tracking prominent objects relatively independent of background types. The extension of the present achievement to improve the recognition accuracy as well as the identification of occluded objects from a crowd formulates the next stage of work.

Paper Details

Date Published: 24 September 2009
PDF: 12 pages
Proc. SPIE 7486, Optics and Photonics for Counterterrorism and Crime Fighting V, 74860G (24 September 2009); doi: 10.1117/12.830489
Show Author Affiliations
Aristeidis Tsitiridis, Cranfield Univ. (United Kingdom)
Peter Yuen, Cranfield Univ. (United Kingdom)
Kan Hong, Cranfield Univ. (United Kingdom)
Tong Chen, Cranfield Univ. (United Kingdom)
Firmin Kam, Cranfield Univ. (United Kingdom)
James Jackman, Cranfield Univ. (United Kingdom)
David James, Cranfield Univ. (United Kingdom)
Mark Richardson, Cranfield Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 7486:
Optics and Photonics for Counterterrorism and Crime Fighting V
Colin Lewis, Editor(s)

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