Probabilistic Multi Hypothesis Tracker for an Event Based Sensor

The Event-Based Sensor (EBS) is a new class of imaging sensor where each pixel independently reports “events” in response to changes in log intensity, rather than outputting image frames containing the absolute intensity at each pixel. Positive and negative events are emitted from the sensor when the change in log intensity exceeds certain controllable thresholds internal to the device. For objects moving through the field of view, a change in intensity can be related to motion. The sensor records events independently and asynchronously for each pixel with a very high temporal resolution, allowing the detection of objects moving very quickly through the field of view. Recently this type of sensor has been applied to the detection of orbiting space objects using a ground-based telescope. This paper describes a method to treat the data generated by the EBS as a classical detect-then-track problem by collating the events spatially and temporally to form target measurements. An efficient multi-target tracking algorithm, the probabilistic multi-hypothesis tracker (PMHT) is then applied to the EBS measurements to produce tracks. This method is demonstrated by automatically generating tracks on orbiting space objects from data collected by the EBS.