EV-Tach: A Handheld Rotational Speed Estimation System With Event Camera.

Rotational speed is one of the important metrics to be measured for calibrating electric motors in manufacturing, monitoring engines during car repairs, detecting faults in electrical appliance and more. However, existing measurement techniques either require prohibitive hardware (e.g., high-speed camera) or are inconvenient to use in real-world application scenarios. In this paper, we propose, EV-Tach , a novel handheld rotational speed estimation system that utilizes emerging imaging sensors known as event cameras or dynamic vision sensors (DVS). The pixels of DVS work independently and trigger an event as soon as a per-pixel intensity change is detected, without global synchronization like conventional RGB cameras. Thus, its unique design features high temporal resolution and generates sparse events, which benefits the high-speed rotation estimation. To achieve accurate and efficient rotational speed estimation, a series of signal processing algorithms are specifically designed for the event streams generated by event cameras on an embedded platform. First, a new cluster-centroids initialization module is proposed to initialize the centroids of the clusters to address the issue that common clustering approaches are easy to fall into a local optimal solution without proper initial centroids. Second, an outlier removal module is designed to suppress the background noise caused by subtle hand movements and host devices vibrations. Third, a coarse-to-fine alignment strategy is proposed with an event stream alignment method to obtain angle of rotation and achieve accurate estimation for rotational speed in a large range. With these bespoke components, EV-Tach is able to extract the rotational speed accurately from the event stream produced by an event camera recording rotary targets. According to our extensive evaluations under controlled and practical experiment settings, the Relative Mean Absolute Error (RMAE) of EV-Tach is as low as $0.3\%_{0}$ , which is comparable to the state-of-the-art laser tachometer under fixed measurement mode. Moreover, EV-Tach is robust to subtle movement of user's hand and dazzling light outdoor, therefore, can be used as a handheld device under challenging lighting condition, where the laser tachometer fails to produce reasonable results. To speed up the processing of EV-Tach and reduce its resource consumption on embedded devices, event stream is significantly downsampled by merging neighboring events while preserving its formation in spatial-temporal domain. At last, we implement EV-Tach on Raspberry Pi and the evaluation results show that the downsampling process preserves the high measurement accuracy while saving the computation speed and energy consumption by approximately 8 times and 30 times in average.