Assessing post-detection filters for a generic pedestrian detector in a tracking-by-detection scheme
Tracking-by-detection becomes more and more popular for visual pedestrian tracking applications. However, it requires accurate and reliable detections in order to obtain good results. In this work, we propose two different post-detection filters designed to enhance the performance of custom person detectors. Using a popular deformable-parts-based pedestrian detector as a baseline, a detailed comparison over multiple test videos is performed and the gain of both algorithms is proven. Further analysis shows that the improved detection outcomes also lead to improved tracking results. We thus found that the usage of the proposed post-detection filters is recommendable as they do not impose a high computational load and are not limited to a specific detector method.