Occluded Pedestrian Detection Based on Joint Attention Mechanism of Channel-wise and Spatial Information
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摘要:
遮挡是行人检测任务中导致漏检发生的主要原因之一，对检测器性能造成了不利影响。为了增强检测器对于遮挡行人目标的检测能力，该文提出一种基于特征引导注意机制的单级行人检测方法。首先，设计一种特征引导注意模块，在保持特征通道间的关联性的同时保留了特征图的空间信息，引导模型关注遮挡目标可视区域；然后，通过注意模块融合浅层和深层特征，从而提取到行人的高层语义特征；最后，将行人检测作为一种高层语义特征检测问题，通过激活图的形式预测得到行人位置和尺度，并生成最终的预测边界框，避免了基于先验框的预测方式所带来的额外参数设置。所提方法在CityPersons数据集上进行了测试，并在Caltech数据集上进行了跨数据集实验。结果表明该方法对于遮挡目标检测准确度优于其他对比算法。同时该方法实现了较快的检测速度，取得了检测准确度和速度的平衡。
Abstract:Pedestrian detector performance is damaged because occlusion often leads to missed detection. In order to improve the detector's ability to detect pedestrian, a single-stage detector based on feature-guided attention mechanism is proposed. Firstly, a feature attention module is designed, which preserves the association between the feature channels while retaining spatial information, and guides the model to focus on visible region. Secondly, the attention module is used to fuse shallow and deep features, then high-level semantic features of pedestrians are extracted. Finally, pedestrian detection is treated as a high-level semantic feature detection problem. Pedestrian location and scale are obtained through heat map prediction, then the final prediction bounding box is generated. This way, the proposed method avoids the extra parameter settings of the traditional anchor-based method. Experiments show that the proposed method is superior to other comparison algorithms for the accuracy of occlusion target detection on CityPersons and Caltech pedestrian database. At the same time, the proposed method achieves a faster detection speed and a better balance between detection accuracy and speed.
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表 1 验证实验条件设置
R (Reasonable) HO (Heavy Occlusion) R+HO (Reasonable+Heavy Occlusion) $v \in [0.65,\infty )$ $v \in [0.20,0.65]$ $v \in [0.20,\infty )$ 
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表 2 注意网络验证结果MR–2(%)
方法 R HO R+HO 文献[16] 16.0 56.7 38.2 Baseline 12.1 41.1 38.1 Baseline+CA 11.8 39.2 37.8 Baseline+CA+SA 11.6 38.5 37.3
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表 3 CityPersons数据集测试结果MR-2(%)
方法 主干网络 Reasonable Heavy Partial Bare 测试时间(s) OR-CNN[11] VGG-16 12.8 55.7 15.3 6.7 – FasterRCNN[21] VGG-16 15.4 – – – – ALFNet[8] ResNet-50 12.0 51.9 11.4 8.4 0.27 CSP[9] ResNet-50 11.0 49.3 10.4 7.3 0.33 CAFL[13] ResNet-50 11.4 50.4 12.1 7.6 – PedJointNet[14] ResNet-50 13.5 52.1 – – – TLL[20] ResNet-50 15.5 53.6 17.2 10.0 – RepLoss[23] ResNet-50 13.2 56.9 16.8 7.6 – 本文方法 ResNet-50 11.6 47.6 9.8 7.5 0.22
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