Dismount tracking and identification from electro-optical imagery

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

et al. 2014

Recognizing activities in wide aerial/overhead imagery remains a challenging problem due in part to low-resolution video and cluttered scenes with a large number of moving objects. In the context of this research, we deal with two unsynchronized data sources collected in real-world operating scenarios: full-motion videos (FMV) and analyst call-outs (ACO) in the form of chat messages (voice-to-text) made by a human watching the streamed FMV from an aerial platform. We present a multi-source multi-modal activity/event recognition system for surveillance applications, consisting of: (1) detecting and tracking multiple dynamic targets from a moving platform, (2) representing FMV target tracks and chat messages as graphs of attributes, (3) associating FMV tracks and chat messages using a probabilistic graph-based matching approach, and (4) detecting spatial-temporal activity boundaries. We also present an activity pattern learning framework which uses the multi-source associated data as training to index a large archive of FMV videos. Finally, we describe a multi-intelligence user interface for querying an index of activities of interest (AOIs) by movement type and geo-location, and for playing-back a summary of associated text (ACO) and activity video segments of targetsof-interest (TOIs) (in both pixel and geo-coordinates). Such tools help the end-user to quickly search, browse, and prepare mission reports from multi-source data.

Section: Mapping Tracks To Graphsmentioning

confidence: 99%

Multi-source Multi-modal Activity Recognition in Aerial Video Surveillance

Hammoud

Şahin

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

et al. 2014

“…, actor attribute) based on motion, blob size and shape. The shape attribute is divided into “car” vs. support utility vehicle “SUV” vs. “truck” for vehicle, and “adult” vs. “child” for human actor/dismount [ 16 ]. Each actor is characterized with a unique color attribute (e.g., black truck, human with red-shirt, etc. )…”

Section: Multi-graph Representation Of a Single Fmv Trackmentioning

confidence: 99%

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

Hammoud

Şahin

et al. 2014

Sensors

Self Cite

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.

“…The ontology content should use a common message passing schema, with fields such as at <time> <place> <{pers, veh, obj}, qty> <activity> that are available through video extraction. Using the schema, results from distributed video tracking [65], sparse scenes [66], person-vehicle interactions [67], and person-vehicle-object-facility models [68], can be updated and reported to ATC airport operations.…”

Section: Activity Schema For Alertingmentioning

confidence: 99%

Enhanced air operations for ground situational awareness

2014 IEEE/AIAA 33rd Digital Avionics Systems Conference (DASC)

Wang

Shen

et al. 2014

Self Cite

Future digital avionics systems will work in complex and cluttered environments which require systems engineering solutions for such applications as airport ground surface management. In this paper, we highlight the use of a L1 video tracker for monitoring activities at an airport. We present methods of information fusion, entity detection, and activity analysis using airport videos for runway detection and terminal events. For coordinated airport security, automated ground surveillance enhances efficient and safe maneuvers for unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs) operation at airport environments.