  Event cameras, which are asynchronous bio-inspired vision sensors, have shown
great potential in a variety of situations, such as fast motion and low
illumination scenes. However, most of the event-based object tracking methods
are designed for scenarios with untextured objects and uncluttered backgrounds.
There are few event-based object tracking methods that support bounding
box-based object tracking. The main idea behind this work is to propose an
asynchronous Event-based Tracking-by-Detection (ETD) method for generic
bounding box-based object tracking. To achieve this goal, we present an
Adaptive Time-Surface with Linear Time Decay (ATSLTD) event-to-frame conversion
algorithm, which asynchronously and effectively warps the spatio-temporal
information of asynchronous retinal events to a sequence of ATSLTD frames with
clear object contours. We feed the sequence of ATSLTD frames to the proposed
ETD method to perform accurate and efficient object tracking, which leverages
the high temporal resolution property of event cameras. We compare the proposed
ETD method with seven popular object tracking methods, that are based on
conventional cameras or event cameras, and two variants of ETD. The
experimental results show the superiority of the proposed ETD method in
handling various challenging environments.
