  There is growing interest in object detection in advanced driver assistance
systems and autonomous robots and vehicles. To enable such innovative systems,
we need faster object detection. In this work, we investigate the trade-off
between accuracy and speed with domain-specific approximations, i.e.
category-aware image size scaling and proposals scaling, for two
state-of-the-art deep learning-based object detection meta-architectures. We
study the effectiveness of applying approximation both statically and
dynamically to understand the potential and the applicability of them. By
conducting experiments on the ImageNet VID dataset, we show that
domain-specific approximation has great potential to improve the speed of the
system without deteriorating the accuracy of object detectors, i.e. up to 7.5x
speedup for dynamic domain-specific approximation. To this end, we present our
insights toward harvesting domain-specific approximation as well as devise a
proof-of-concept runtime, AutoFocus, that exploits dynamic domain-specific
approximation.
