RASW: A run-time adaptive sliding window to improve Viola-Jones object detection

In recent years accurate algorithms for detecting objects in images have been developed. Among these algorithms, the object detection scheme proposed by Viola and Jones gained great popularity, especially after the release of high-quality face classifiers by the OpenCV group. However, as any other sliding-window based object detector, it is affected by a strong increase in the computational cost as the size of the scene grows. Especially in real-time applications, a search strategy based on a sliding window can be computationally too expensive. In this paper, we propose an efficient approach to adapt at run time the sliding window step size in order to speed-up the detection task without compromising the accuracy. We demonstrate the effectiveness of the proposed Run-time Adaptive Sliding Window (RASW) in improving the performance of Viola-Jones object detection by providing better throughput-accuracy tradeoffs. When comparing our approach with the OpenCV face detection implementation, we obtain up to 2.03x speedup in frames per second without any loss in accuracy.