Temporal integration for on-board stereo-based pedestrian detection

Pedestrian detection is a difficult task for the following reasons. Firstly, pedestrians have many variations of size, pose, clothing, and motion. Secondly, the background tends to be crowded. Thirdly, many pedestrian-like patterns are observed in real environments. As a result, even if state-of-the-art pattern recognition method is used, a frame-by-frame detection has many false positives and negatives. This article presents a method of temporal integration for stereo-based pedestrian detection for improving the detection performance. In our method, a pedestrian is detected by evaluating consistency of the extracted pedestrian candidate for a short period of time. In order to get the consistency, the pedestrian candidate at each frame is combined with temporally corresponded ones through a hypothesis selection process. We demonstrate the effectiveness of our method by checking a recall and false positive number with 20,000 frames recorded in complex urban environments and public data sets. The proposed method reduces the number of false detection to one hundredth with holding recall.