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CCT: Context and Colorization for Tracking

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: 140D0420C0048
Agency Tracking Number: D2-2449
Amount: $997,313.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: HR001119S0035-13
Solicitation Number: DARPA HR001119S0035-13
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-03-06
Award End Date (Contract End Date): 2021-12-15
Small Business Information
15400 Calhoun Drive Suite 190
Rockville, MD 20855
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Naresh Cuntoor
 Senior Research Scientist
 (301) 294-4768
Business Contact
 Mark James
Phone: (301) 294-5200
Research Institution

Video-based multi-camera tracking has witnessed tremendous progress in recent years. Deep learning has led to dramatic gains in speed and accuracy of tracking algorithms. Similar success is seen in RF-based and other single sensor-based trackers. Multi-sensor tracking however, remains a challenge. The problem is especially difficult when sensors provide detections of unknown confidence, which makes it difficult to leverage the successes of existing Bayesian approaches to tracking. To address this problem, we propose a deep learning-based semi-supervised approach which learns to map heterogeneous features in a common embedding space. Comparative evaluation will be used to demonstrate the benefits of the approach.

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

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