Parallel attentive visual tracking

Roberts, Jonathan M. & Charnley, D. (1994) Parallel attentive visual tracking. Engineering Applications of Artificial Intelligence, 7(2), pp. 205-215.

View at publisher

Abstract

The research reported here addresses the problem of detecting and tracking independently moving objects from a moving observer in real-time, using corners as object tokens. Corners are detected using the Harris corner detector, and local image-plane constraints are employed to solve the correspondence problem. The approach relaxes the restrictive static-world assumption conventionally made, and is therefore capable of tracking independently moving and deformable objects. Tracking is performed without the use of any 3-dimensional motion model. The technique is novel in that, unlike traditional feature-tracking algorithms where feature detection and tracking is carried out over the entire image-plane, here it is restricted to those areas most likely to contain-meaningful image structure. Two distinct types of instantiation regions are identified, these being the “focus-of-expansion” region and “border” regions of the image-plane. The size and location of these regions are defined from a combination of odometry information and a limited knowledge of the operating scenario. The algorithms developed have been tested on real image sequences taken from typical driving scenarios. Implementation of the algorithm using T800 Transputers has shown that near-linear speedups are achievable, and that real-time operation is possible (half-video rate has been achieved using 30 processing elements).

Impact and interest:

2 citations in Scopus
Search Google Scholar™
4 citations in Web of Science®

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 83310
Item Type: Journal Article
Refereed: Yes
Keywords: Attentive vision, Computer-vision, Image processing, Parallel processing, Vehicle obstacle detection
DOI: 10.1016/0952-1976(94)90024-8
ISSN: 0952-1976
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 1994 Elsevier Science Ltd
Deposited On: 14 Apr 2015 00:11
Last Modified: 14 Apr 2015 00:11

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page