Multi-Modal Detection Fusion on a Mobile UGV for Wide-Area, Long-Range Surveillance
| Autor: | Eran Swears, Keith Fieldhouse, Paul Tunison, Adam Romlein, Matt Brown, Anthony Hoogs |
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| Rok vydání: | 2019 |
| Předmět: |
Unmanned ground vehicle
Computer science business.industry 010401 analytical chemistry Detector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Field of view 02 engineering and technology Image segmentation 01 natural sciences 0104 chemical sciences Salient 0202 electrical engineering electronic engineering information engineering RGB color model Clutter 020201 artificial intelligence & image processing Computer vision Artificial intelligence Focus (optics) business |
| Zdroj: | WACV |
| DOI: | 10.1109/wacv.2019.00207 |
| Popis: | We introduce a self-contained, mobile surveillance system designed to remotely detect and track people in real time, at long ranges, and over a wide field of view in cluttered urban and natural settings. The system is integrated with an unmanned ground vehicle, which hosts an array of four IR and four high-resolution RGB cameras, navigational sensors, and onboard processing computers. High-confidence, low-false-alarm-rate person tracks are produced by fusing motion detections and single-frame CNN person detections between co-registered RGB and IR video streams. Processing speeds are increased by using semantic scene segmentation and a tiered inference scheme to focus processing on the most salient regions of the 43° x 7.8° composite field of view. The system autonomously produces alerts of human presence and movement within the field of view, which are disseminated over a radio network and remotely viewed on a tablet computer. We present an ablation study quantifying the benefits that multi-sensor, multi-detector fusion brings to the problem of detecting people in challenging outdoor environments with shadows, occlusions, clutter, and variable weather conditions. |
| Databáze: | OpenAIRE |
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