Shadow extraction and application in pedestrian detection

Abstract

We find shadows in many images and videos. Traditionally, shadows are considered as noises because they make hurdles for visual tasks such as detection and tracking. In this work, we show that shadows are helpful in pedestrian detection instead. Occlusions make pedestrian detection difficult. Existing shape-based detection methods can have false-positives on shadows since they have similar shapes with foreground objects. Appearance-based detection methods cannot detect heavily occluded pedestrians. To deal with these problems, we use appearance, shadow, and motion information simultaneously in our method. We detect pedestrians using appearance information of pedestrians and shape information of shadow regions. Then, we filter the detection results based on motion information if available. The proposed method gives low false-positives due to the integration of different features. Moreover, it alleviates the problem brought by occlusions since shadows can still be observable when foreground objects are occluded. Our experimental results show that the proposed algorithm provides good performance in many difficult scenarios. © 2014 Wang and Yagi.