Automatic Association of Chats and Video Tracks for Activity Learning and Recognition in Aerial Video Surveillance

Sensors

Riad I. Hammoud¹  Cem S. Sahin¹  Erik P. Blasch²  Bradley J. Rhodes¹ 

[1]BAE Systems, Burlington, MA 01803, USA

[2] E-Mails:

[3]Air Force Research Lab, Rome, NY 13441, USA

[4] E-Mail:

关键词: activity recognition;    FMV tracking;    ATR;    fusion;    surveillance;    pattern learning;    features;    registration;    geo-registration;   

DOI  :  10.3390/s141019843

来源: mdpi

【 摘 要 】

We describe two advanced video analysis techniques, including video-indexed by voice annotations (VIVA) and multi-media indexing and explorer (MINER). VIVA utilizes analyst call-outs (ACOs) in the form of chat messages (voice-to-text) to associate labels with video target tracks, to designate spatial-temporal activity boundaries and to augment video tracking in challenging scenarios. Challenging scenarios include low-resolution sensors, moving targets and target trajectories obscured by natural and man-made clutter. MINER includes: (1) a fusion of graphical track and text data using probabilistic methods; (2) an activity pattern learning framework to support querying an index of activities of interest (AOIs) and targets of interest (TOIs) by movement type and geolocation; and (3) a user interface to support streaming multi-intelligence data processing. We also present an activity pattern learning framework that uses the multi-source associated data as training to index a large archive of full-motion videos (FMV). VIVA and MINER examples are demonstrated for wide aerial/overhead imagery over common data sets affording an improvement in tracking from video data alone, leading to 84% detection with modest misdetection/false alarm results due to the complexity of the scenario. The novel use of ACOs and chat messages in video tracking paves the way for user interaction, correction and preparation of situation awareness reports.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.