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.
Highlights
Shadows can be found in many images and videos
We assume that η(t) is a learning rate set for our recursive filter
We have demonstrated the power of shadow information for pedestrian detection in outdoor environments
Summary
Shadows can be found in many images and videos. Shadows are formulated when direct light from a light source cannot reach due to obstruction by an opaque object. We propose a detection algorithm considering shadows as helpful information in pedestrian detection. Object detection and tracking can be performed based on background subtraction results. Dalal and Triggs [3] proposed an appearance-based object detection method using histogram of gradient orientations. Dalal et al [8] normalized optical flow in video frames and applied motion information into pedestrian detection. Given a color vector without cast shadows, many shadow detection algorithms assume that the vector under cast shadows keeps the original vector direction This assumption is not correct in outdoor environments because the ambient light source is blue. Background subtraction results include foreground objects and shadows. To separate shadows from foreground blobs, we apply a morphological close filter on background subtraction results to fill the gaps.
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