Robust Multiple Target Tracking

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$99,041.00
Award Year:
2007
Program:
STTR
Phase:
Phase I
Contract:
W911NF-07-C-0065
Award Id:
83229
Agency Tracking Number:
A074-007-0091
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
11600 Sunrise Valley Drive, Suite # 290, Reston, VA, 20191
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
038732173
Principal Investigator:
Mun Lee
Research Scientist
(703) 654-9300
mlee@objectvideo.com
Business Contact:
Paul Brewer
VP, New Technology
(703) 654-9314
pbrewer@objectvideo.com
Research Institution:
UNIV. OF SOUTHERN CALIFORNIA
Ramakant Nevatia
PHE 204, MC-0273
3737 Watt Way
Los Angeles, CA, 90089
(213) 740-6427
Nonprofit college or university
Abstract
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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