| Citation: | DAI P Z，LIU X，ZHANG X，et al. An iterative pedestrian detection method sensitive to historical information features[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（9）：2493-2500 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0665
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Object detection algorithms based on deep learning usually need to use post-processing methods such as non-maximum suppression to filter the prediction box, and can not balance the detection accuracy and recall rate of the model in the crowded pedestrian scene. Although the iterative detection method can solve the problems caused by non-maximum suppression methods, repeated detection will also limit the performance of the model. In this paper, a pedestrian iterative detection method sensitive to historical information features is proposed. Firstly, the weighted historical information characteristics (WHIC) is introduced to improve the feature discrimination. Second, the historical information feature extraction module (HIFEM) suggested in this paper is utilized to obtain and fuse historical information features of various scales into the main network for multi-scale detection, increasing the sensitivity of the model to the historical information features. This method can effectively suppress the generation of repeated detection frames. Experimental results show that the proposed method achieves the best detection accuracy and recall on CrowdHuman and WiderPerson.
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