Real-Time Temporal Frequency Detection in FPGA Using Event-Based Vision Sensor

A dynamic vision sensor (DVS) is a new type of vision sensor in which each pixel acts as a motion sensor and generates highly time-accurate events when it detects movement in the scene. The high temporal precision of these types of vision sensors allows the extraction of different low-level temporal features, which is not possible when using a frame-based camera. Hierarchical vision-processing systems use low-level features to recognize a higher level of abstraction. One of the lowlevel features that can be extracted with DVS is the temporal frequency. This feature can be used along with other visual features for more accurate object recognition when the object has rotating parts, such as a quadcopter. This work is an extension of our previous work, wherein we proposed an algorithm to extract this temporal low-level feature by using a DVS. In this work, we proposed a digital circuit with a small footprint to extract the frequency of rotating objects in real time with very low latency. We have synthesized the digital circuit in Spartan-6 field-programmable gate array (FPGA) and also in UMC 180-nm technology to measure the performance, power consumption, and occupied area. MATLAB and Verilog codes for this work are available for academic purposes upon request.