Occlusion Handling and Multi-Scale Pedestrian Detection Based on Deep Learning: A Review

Pedestrian detection is an important branch of computer vision, and has important applications in the fields of autonomous driving, artificial intelligence and video surveillance. With the rapid development of deep learning and the proposal of large-scale datasets, pedestrian detection has reached a new stage and has achieved better performance. However, the performance of state-of-the-art methods is far behind expectations, especially when occlusion and scale variance exist. Therefore, many works focused on occlusion and scale variance have been proposed in the past few years. The purpose of this article is to make a detailed review of recent progress in pedestrian detection. First, a brief progress of pedestrian detection in the past two decades is summarized. Second, recent deep learning methods focusing on occlusion and scale variance are analyzed. Moreover, the popular datasets and evaluation methods for pedestrian detection are introduced. Finally, the development trends in pedestrian detection are discussed.

Abstract licence: CC BY-NC-ND

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