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To better detect pedestrians of various scales, deep multi-scale methodsusually detect pedestrians of different scales by different in-network layers.However, the semantic levels of features from different layers are usuallyinconsistent. In this paper, we propose a multi-branch and high-level semanticnetwork by gradually splitting a base network into multiple different branches.As a result, the different branches have the same depth and the output featuresof different branches have similarly high-level semantics. Due to thedifference of receptive fields, the different branches are suitable to detectpedestrians of different scales. Meanwhile, the multi-branch network does notintroduce additional parameters by sharing convolutional weights of differentbranches. To further improve detection performance, skip-layer connectionsamong different branches are used to add context to the branch of relativelysmall receptive filed, and dilated convolution is incorporated into partbranches to enlarge the resolutions of output feature maps. When they areembedded into Faster RCNN architecture, the weighted scores of proposalgeneration network and proposal classification network are further proposed.Experiments on KITTI dataset, Caltech pedestrian dataset, and Citypersonsdataset demonstrate the effectiveness of proposed method. On these pedestriandatasets, the proposed method achieves state-of-the-art detection performance.Moreover, experiments on COCO benchmark show the proposed method is alsosuitable for general object detection.

Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection