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Target Tracking via Deep Learning

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8650-18-C-1739
Agency Tracking Number: F17A-027-0146
Amount: $749,964.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: AF17A-T027
Solicitation Number: 2017.0
Solicitation Year: 2017
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-09-27
Award End Date (Contract End Date): 2020-09-27
Small Business Information
600 West Cummings Park
Woburn, MA 01801
United States
DUNS: 964928464
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 MIchael Braun
 (781) 503-3295
Business Contact
 Allan Lew
Phone: (339) 999-2071
Research Institution
 Rochester Institute of Technology
 Laura Kleiman
141 Lomb Memorial Drive
Rochester, NY 14623
United States

 (585) 475-2262
 Domestic Nonprofit Research Organization

To address the challenge of long-term tracking, through extended occlusions and significant appearance changes, we propose to continue developing DC-CAT, a Deep Convolutional neural network (CNN) based Confuser-Aware high value target (HVT) Tracker.The DC-CAT system combines a state-of-the-art CNN-based adaptive HVT tracker with a CNN-based pre-trained generic target detector, in a deep-feature-aided multi-target tracking (MTT) framework. The generic target proposals outputted by this second detector enable the tracker to (1) detect when the HVT is occluded and avoid updating its adaptive discriminative model during the occlusion, and (2) re-acquire the target afterward, even if the targets appearance has changed significantly. The multi-target tracker provides awareness of confusers, greatly reducing the likelihood that the tracker will be diverted to a confuser during the occlusion event or in dense traffic.Our team, consisting of Systems & Technology Research (STR) and Rochester Institute of Technology (RIT), leverages extensive experience in tracking algorithm development for real-time airborne surveillance applications, as well as our expertise in deep-learning-based object detection and tracking, to develop a robust tracking capability that is adaptable to a wide range of target, scene and sensor operating conditions.

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

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