摘要
The data from event cameras not only portray contours of moving objects but also contain motion information inherently.Herein,motion information can be used in event-based and frame-based object trackers to ease the challenges of occluded objects and data association,respectively.In the event-based tracker,events within a short interval are accumulated.Within the interval,the histogram of local time measurements(or‘motion histogram’)is proposed as the feature to describe the target and candidate regions.Then the mean-shift tracking approach is used by shifting the tracker towards similarity maximisation on motion histograms between target and candidate regions.As for the frame-based tracker,given the assumption that a single object moves at a constant velocity on the image plane,the distribution of local timestamps is modelled,followed by which object-level velocities are obtained from parameter estimation.We then build a Kalman-based ensemble,in which object-level velocities are deemed as an additional measurement on top of object detection results.Experiments have been conducted to measure the performance of proposed trackers based on our self-collected data.Thanks to the assistance from motion information,the event-based tracker successfully differentiates partially overlapped objects with distinct motion profiles;The inter-frame tracker avoids data association failure on fast-moving objects and leads to fast convergence on object velocity estimation.
