Moving Object Detection for Event-based Vision using k-means Clustering

Event-based cameras are bio-inspired sensors that mimic the working of the human eye (Gallego et al. [2020]). While frame-based cameras capture images at a definite frame rate which is determined by an external clock, each pixel in event-based cameras memorizes the log intensity each time an event is sent and simultaneously monitors for a sufficient change in magnitude from this memorized threshold value (Gallego et al. [2020]). The event is recorded by the camera and is transmitted by the sensor in the form of its location {x, y}, its time of occurrence (timestamp) t and its polarity p (taking a binary value 1 or 1, representing whether the pixel is brighter or darker) (Chen et al. [2020]). The working of an event-based camera is shown in Figure 1. The sensors used in event-based cameras are data-driven, for their output depends on the amount of motion or brightness change in the scene (Gallego et al. [2020]). Higher is the motion, higher is the number of events generated. The events are recorded in microsecond resolution and are transmitted in sub-millisecond latency, making these sensors react quickly to visual stimuli (Gallego et al. [2020]). Thus, while frame-based cameras capture the absolute brightness of a scene, event-based cameras capture the per-pixel brightness asynchronously, making traditional computer vision algorithms inapplicable to be implemented for processing the event data. Detection of moving objects is an important task in automation, where a computer differentiates in between a moving object and a stationary one.