迭代Faster R-CNN的密集行人检测

doi:10.3778/j.issn.1002-8331.2206-0315

摘要/Abstract

摘要： 行人检测是利用计算机视觉技术判断图像或者视频序列中是否存在行人并给予精确定位。针对行人检测在密集场景下普遍存在的行人间遮挡问题，提出基于迭代Faster R-CNN的密集行人检测模型，利用一种IterDet迭代方案对Faster R-CNN进行改进，有效解决非极大值抑制（NMS）算法及其改进在选择精确度和召回率之间平衡点的难题。同时利用递归金字塔结构（RFP）进一步增强模型提取特征能力。在具有挑战性的WiderPerson和CrowdHuman数据集上进行训练和验证，实验结果表明，该模型相比Faster R-CNN在精度和召回率显著提升的同时，漏检率也明显降低。尤其在WiderPerson数据集上召回率、精度、漏检率等性能指标分别达到了97.65%、91.29%、40.43%的SOTA结果。

关键词: 行人检测, 密集场景, 遮挡问题, Faster R-CNN, 迭代方案

Abstract: Pedestrian detection uses computer vision technology to determine whether there are pedestrians in the image or video sequence and give accurate positioning. In this paper, a dense pedestrian detection model based on iterative Faster R-CNN is proposed to solve the common pedestrian occlusion problem in dense scenes. An IterDet iteration scheme is used to improve Faster R-CNN, which effectively solves the problem of choosing a balance between precision and recall for the NMS and its improvement. At the same time, the recursive pyramid structure is used to further enhance the feature extraction ability of the model. This paper trains and validates on the challenging WiderPerson and CrowdHuman datasets. The experimental results show that compared with Faster R-CNN, the model in this paper significantly improves the precision and recall, but also significantly reduces the mMR. Especially on the WiderPerson dataset, the recall, precision, and mMR has reached SOTA results of 97.65%, 91.29%, and 40.43%, respectively.

Key words: pedestrian detection , dense scenes, occlusion problem, Faster R-CNN, iterative scheme

贺宇哲, 徐光美, 何宁, 于海港, 张人, 晏康. 迭代Faster R-CNN的密集行人检测[J]. 计算机工程与应用, 2023, 59(21): 214-221.

HE Yuzhe, XU Guangmei, HE Ning, YU Haigang, ZHANG Ren, YAN Kang. Dense Pedestrian Detection with Iterative Faster R-CNN[J]. Computer Engineering and Applications, 2023, 59(21): 214-221.

参考文献

[1] 盖荣丽，蔡建荣，王诗宇，等.卷积神经网络在图像识别中的应用研究综述[J].小型微型计算机系统，2021，42（9）：1980-1984.
GAI R L，CAI J R，WANG S Y，et al.Research review on image recognition based on deep learning[J].Journal of Chinese Computer Systems，2021，42（9）：1980-1984.
[2] 曹帅，张晓伟，马健伟.基于跨尺度特征聚合网络的多尺度行人检测[J].北京航空航天大学学报，2020，46（9）：1786-1796.
CAO S，ZHANG X W，MA J W.Trans-scale feature aggregation network for multiscale pedestrian detection[J].Journal of Beijing University of Aeronautics and Astronautics，2020，46（9）：1786-1796.
[3] REDMON J，FARHADI A.Yolov3：an incremental improvement[C]//Proceedings of the IEEE International Conference on Computer Vision，2018.
[4] LIN T Y，GOYAL P，GIRSHICK R，et al.Focal loss for dense object detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2018，42（2）：318-327.
[5] REN S Q，HE K M，GIRSHICK R，et al.Faster R-CNN：towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems，2015：91-99.
[6] CAI Z，VASCONSELOS N.Cascade r-cnn：delving into high quality object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition，2018.
[7] 邓杰，万旺根.基于改进YOLOv3的密集行人检测[J].电子测量技术，2021，44（11）：90-95.
DENG J，WAN W G.Dense pedestrian detection based on improved YOLOv3[J].Electronic Measurement Technology，2021，44（11）：90-95.
[8] RUKHOVICH D，SODIIUK K，GALEEV D，et al.Iterdet：iterative scheme for object detection in crowded environments[C]//Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition（SPR） and Structural and Syntactic Pattern Recognition（SSPR）.Cham：Springer，2021：344-354.
[9] QIAO S，CHEN L C，YUILLE A.Detectors：detecting objects with recursive feature pyramid and switchable atrous convolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2021：10213-10224.
[10] ZHANG S，XIE Y，WAN J，et al.Widerperson：a diverse dataset for dense pedestrian detection in the wild[J].IEEE Transactions on Multimedia，2019，22（2）：380-393.
[11] SHAO S，ZHAO Z，LI B，et al.Crowdhuman：a benchmark for detecting human in a crowd[J].arXiv：1805. 00123，2018.
[12] ROTHE R，GUILLAUMIN M.Non-maximum suppression for object detection by passing messages between windows[C]//Asian Conference on Computer Vision.Cham：Springer，2014：290-306.
[13] HOSANG J，BENENSON R.Learning non-maximum suppression[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：4507-4515.
[14] BODLA N，SINGH B，CHELLAPPA R，et al.Soft-NMS—improving object detection with one line of code[C]//Proceedings of the IEEE International Conference on Computer Vision，2017：5561-5569.
[15] LIU S，HUANG D，WANG Y.Adaptive nms：refining pedestrian detection in a crowd[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2019：6459-6468.
[16] HUANG X，GE Z，JIE Z，et al.NMS by representative region：towards crowded pedestrian detection by proposal pairing[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2020：10750-10759.
[17] WANG X，XIAO T，JIANG Y，et al.Repulsion loss：detecting pedestrians in a crowd[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：7774-7783.
[18] ZHANG S，WEN L，BIAN X，et al.Occlusion-aware R-CNN：detecting pedestrians in a crowd[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：637-653.
[19] GE Z，JIE Z，HUANG X，et al.Ps-rcnn：detecting secondary human instances in a crowd via primary object suppression[C]//2020 IEEE International Conference on Multimedia and Expo（ICME），2020：1-6.
[20] XU Z，LI B，YUAN Y，et al.Beta r-cnn：looking into pedestrian detection from another perspective[C]//Advances in Neural Information Processing Systems，2020.
[21] WANG J，SONG L，LI Z，et al.End-to-end object detection with fully convolutional network[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，2021：15849-15858.
[22] LIN T Y，DOLLAR P，GIRSHICK R，et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：2117-2125.
[23] CORDTS M，OMRAN M，RAMOS S，et al.The cityscapes dataset for semantic urban scene understanding[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：3213-3223.