Retrieving gray-level information from a Binary Sensor and its application to gesture detection

Abstract

We report on the use of a CMOS Contrast-based Binary Vision Sensor (CBVS), with embedded contrast extraction, for gesture detection applications. The first advantage of using this sensor over commercial imagers is a dynamic range of 120dB, made possible by a pixel design that effectively performs auto-exposure control. Another benefit is that, by only delivering the pixels detecting a contrast, the sensor requires a very limited bandwidth.We leverage the sensor's fast 150µs readout speed, to perform multiple reads during a single exposure; this allows us to estimate gray-level information from the otherwise binary pixels. As a use case for this novel readout strategy, we selected in-car gesture detection, for which we carried out preliminary tests showing encouraging results.

Deep Learning with Energy-efficient Binary Gradient Cameras

“…The first, independent of the actual number of active pixels, is the power required to scan the sensor and amounts to 0.0024µW/pixel. The second is the power required to deliver the addresses of the active pixels, and is 0.0195µW/pixel [8]. At 30fps, this power corresponds to 7.3pJ/pixels.…”

“…Designing a sensor with a variable number of quantization bits, while allowing for low power consumption, could be challenging. However, graylevel information can be extracted from a binary gradient camera by accumulating multiple frames, captured at a high frame rate, and by combining them into a sum weighted by the time of activation [8].…”

“…where N is the number of quantization levels, P scan is the power required to scan all the rows of the sensor, and P deliver is the power to deliver the data out of the sensor, which depends on the number of active pixels [8]. Despite the fact that P deliver is an order of magnitude larger than P scan , Equation 3shows that the total power quickly grows with the number of bits.…”