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A new challenging vision system has recently gained prominence and proven its capacities compared to traditional imagers: the paradigm of event-based vision. Instead of capturing the whole sensor area in a fixed frame rate as in a frame-based camera, Spike sensors or event cameras report the location and the sign of brightness changes in the image. Despite the fact that the currently available spatial resolutions are quite low (640x480 pixels) for these event cameras, the real interest is in their very high temporal resolution (in the range of microseconds) and very high dynamic range (up to 140 dB). Thanks to the event-driven approach, their power consumption and processing power requirements are quite low compared to conventional cameras. This latter characteristic is of particular interest for embedded applications especially for situational awareness. The main goal for this project is to detect and to track activity zones from the spike event stream, and to notify the standard imager where the activity takes place. By doing so, automated situational awareness is enabled by analyzing the sparse information of event-based vision, and waking up the standard camera at the right moments, and at the right positions i.e. the detected regions of interest. We demonstrate the capacity of this bimodal vision approach to take advantage of both cameras: spatial resolution for standard camera and temporal resolution for event-based cameras. An opto-mechanical demonstrator has been designed to integrate both cameras in a compact visual system, with embedded Software processing, enabling the perspective of autonomous remote sensing. Several field experiments demonstrate the performances and the interest of such an autonomous vision system. The emphasis is placed on the ability to detect and track fast moving objects, such as fast drones. Results and performances are evaluated and discussed on these realistic scenarios.
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