Abstract
We present Progressive Refinement Network (PRNet), a novel single-stage detector that tackles occluded pedestrian detection. Motivated by human’s progressive process on annotating occluded pedestrians, PRNet achieves sequential refinement by three phases: Finding high-confident anchors of visible parts, calibrating such anchors to a full-body template derived from occlusion statistics, and then adjusting the calibrated anchors to final full-body regions. Unlike conventional methods that exploit predefined anchors, the confidence-aware calibration offers adaptive anchor initialization for detection with occlusions, and helps reduce the gap between visible-part and full-body detection. In addition, we introduce an occlusion loss to up-weigh hard examples, and a Receptive Field Backfeed (RFB) module to diversify receptive fields in early layers that commonly fire only on visible parts or small-size full-body regions. Experiments were performed within and across CityPersons, ETH, and Caltech datasets. Results show that PRNet can match the speed of existing single-stage detectors, consistently outperforms alternatives in terms of overall miss rate, and offers significantly better cross-dataset generalization. Code is available (https://github.com/sxlpris).
X. Song and K. Zhao—These authors contributed equally.
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Acknowledgement
Research reported in this paper was supported in part by the Natural Science Foundation of China under grant 61701032 & 62076036 to KZ, and Beijing Municiple Science and Technology Commission project under Grant No.Z181100001918005 to XS and HZ. We thank Jayakorn Vongkulbhisal for helpful comments.
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Song, X., Zhao, K., Chu, WS., Zhang, H., Guo, J. (2020). Progressive Refinement Network for Occluded Pedestrian Detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12368. Springer, Cham. https://doi.org/10.1007/978-3-030-58592-1_3
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