| Citation: | CAO Shuai, ZHANG Xiaowei, MA Jianweiet al. Trans-scale feature aggregation network for multiscale pedestrian detection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1786-1796. doi: 10.13700/j.bh.1001-5965.2020.0069(in Chinese)
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Space scale variation of pedestrian instance is one of the main bottlenecks affecting pedestrian detection performance. For this issue, a Trans-Scale Feature Aggregation Network (TS-FAN) is proposed to effectively deal with multi-scale pedestrian detection. First, in view of the feature differences among different scale spaces, we introduce a scale compensation strategy based on multi-path Region Proposal Network (RPN). According to the effectiveness of the convolutional feature layers of different scales, a series of candidate regional scale sets are generated adaptively from the feature maps corresponding to the size of the receptive field. Second, considering the semantic complementarity of convolutional features at different levels, a trans-scale feature aggregation module is proposed to effectively aggregate with semantic robustness highllevel features and with accurate location information of low-level features and achieve enhanced representation ability of convolutional features, by aggregating horizontal connection, top-down path and bottom-up path. Finally, combining the multi-path RPN scale compensation strategy and trans-scale feature aggregation module, we construct a multi-scale pedestrian detection network by adaptive scale perception. The experimental results show that, compared with the state-of-the-art method TLL-TFA, the log-average miss rate of pedestrian detection on widely-used Caltech dataset is reduced to 26.21% (increased by 11.94%) for whole-scale pedestrians (above 20 pixel in height), and 47.30% (increased by 12.79%) for small-scale pedestrian (between 20-30 pixels in height). And the similar improvement is also achieved on ETH dataset with drastic scale variation.
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