Real-time automated detection, tracking, classification, and geolocation of dismounts using EO and IR FMV

J. Muncaster; G. Collins; J. Waltman

doi:10.1117/12.2180396

21 May 2015 Real-time automated detection, tracking, classification, and geolocation of dismounts using EO and IR FMV

J. Muncaster, G. Collins, J. Waltman

Proceedings Volume 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V; 94730N (2015) https://doi.org/10.1117/12.2180396
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

The VideoPlus^®-Aware (VPA) system enables autonomous video-based target detection, tracking and classification. The system stabilizes video and operates completely autonomously. A statistical background model enables robust acquisition of moving targets, while stopped targets are tracked using feature-based detectors. An ensemble classifier is trained for automated detection and classification of dismounts (i.e., humans) and a planar scene model is used to both improve system performance and reduce false positives. A formal evaluation of the VPA system was performed by the government, to quantify the system’s abilities to detect, track, and classify, humans. The evaluation provided 811 separate data points gathered over a period of four days with an overall probability of sensing of 99.9%. The probability of detection was 86.2% and the percentage of correct action classification was 82%. The data provided a False Alarm Rate of 0 per hour and Nuisance Alarm Rate of 0.72 per hour. Dismounts were reliably classified with pixel heights as low as 25 pixels. Real-time automated detection, tracking, and classification of targets with low false positive rates was achieved, even with few pixels on target. The planar scene model based optimizations were sufficient to dramatically reduce the runtime of sliding-window classifiers.

Citation Download Citation

J. Muncaster, G. Collins, and J. Waltman "Real-time automated detection, tracking, classification, and geolocation of dismounts using EO and IR FMV", Proc. SPIE 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V, 94730N (21 May 2015); https://doi.org/10.1117/12.2180396

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KEYWORDS

Target detection

Cameras

Sensors

Video

Picosecond phenomena

Detection and tracking algorithms

Visualization

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