In this paper, we describe the design and implementation of a computationally efficient system for detecting moving objects on a moving platform which can be deployed on small, lightweight, low-cost and power-efficient hardware. The primary application of the payload system is that of performing real-time on-board autonomous object detection of moving objects from videos stream taken from a camera mounted to an unmanned aerial vehicle (UAV). The implemented object detection algorithms utilise recursive density estimation and evolving local means clustering to perform change and object detection of moving objects without prior knowledge. Furthermore, experiments are presented which demonstrate that the introduced system is able to detect, by on-board processing, any moving objects from a UAV in real time while at the same time sending only important data to a control station located on the ground with minimal time to set up and become operational.
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This work was funded under the MODs Centre for Defence Enterprise themed call for Generic Enablers for Low Size, Weight, Power and Cost (SWAPC), contact reference DSTLX1000082760. RTSDE was developed in EntelSenSys Ltd as a subcontractor in this work. The authors would like to thank Dr. Asmar Khan (C code and first experiments) and Mr. Ashley Wilding (Beagle board implementation) for their contribution to AURORA Project.
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Angelov, P., Sadeghi-Tehran, P. & Clarke, C. AURORA: autonomous real-time on-board video analytics. Neural Comput & Applic 28, 855–865 (2017). https://doi.org/10.1007/s00521-016-2315-7
- Autonomous objects detection
- Unmanned aerial vehicle
- Evolving clustering
- Video analytic
- Linear motion model