Robust Multiple Target Tracking

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911NF-07-C-0065
Agency Tracking Number: A074-007-0091
Amount: $99,041.00
Phase: Phase I
Program: STTR
Awards Year: 2007
Solicitation Year: 2007
Solicitation Topic Code: A07-T007
Solicitation Number: N/A
Small Business Information
11600 Sunrise Valley Drive, Suite # 290, Reston, VA, 20191
DUNS: 038732173
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Mun Wai Lee
 Research Scientist
 (703) 654-9300
Business Contact
 Paul Brewer
Title: VP, New Technology
Phone: (703) 654-9314
Research Institution
 Ramakant Nevatia
 PHE 204, MC-0273
3737 Watt Way
Los Angeles, CA, 90089
 (213) 740-6427
 Nonprofit college or university
ObjectVideo and University of Southern California propose to develop innovative algorithms and demonstrate a framework for robust and efficient visual tracking using Adaboost-based target detection methods, kernel-based tracker, and robust method for multi-frame data-association. The key issues of multiple target tracking are: variations in target shape and appearance due to camera viewpoint and illumination condition, non-linear target motion, self and inter-occlusion, image noise and distortion, low image contrast, and high scene clutter. We identify three main technical objectives: (i) accurate detection of targets, (ii) robust online tracking, and (iii) persistent tracking of multiple targets across multiple frames. To achieve these objectives, first, we will develop algorithms for target detection based on the AdaBoost technique, boosted-tree multi-view classifier, and using edge-based features including edgelets and histogram-of-gradients. Second, we will design a collaborative multiple-kernel algorithm for robust online tracking. Third, we will design a multi-frame algorithm for data association for persistent tracking and error correction. These algorithms address different functional requirements of a tracking system in a coordinated framework to achieve improved and efficient overall performance. We will validate the developed algorithms with thorough performance evaluation and quantitative analysis under different environments and scenarios.

* Information listed above is at the time of submission. *

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