Adaptive Sliding-Window Strategy for Vehicle Detection in Highway Environments

Abstract

In highway traffic monitoring systems, vehicle detection is one of the most important tasks. To automatically detect vehicles in general CCTV environments, we should effectively handle the problems caused by the following three issues: severe variation in appearance, ambiguities in location, and ambiguities in size. Over the last decade, these issues have been mainly addressed by employing a sliding-window-based image search. Although it is quite effective in many applications, the manual procedure of initializing necessary size models and high computational costs impede the automation of monitoring systems. To remedy these drawbacks, we propose an adaptive sliding-window strategy, where useful size templates are continuously modeled for a given scene, and the detection is performed by adaptively deforming a sliding window based on the obtained templates. This strategy allows us not only to avoid complex user interferences but also to reduce computational costs for vehicle detection. Experiments on real-world highway environments show that the proposed strategy achieved about 23% improved detection accuracy with 43% reduced processing time over the state-of-the-art sliding-window approach without any user assistance.