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首页> 外文期刊>Journal of visual communication & image representation >Dynamic Dual-Peak Network: A real-time human detection network in crowded scenes
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Dynamic Dual-Peak Network: A real-time human detection network in crowded scenes

机译：动态双峰网络：拥挤场景中的实时人类检测网络

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摘要

Human detection in crowded scenes is challenging since the objects occlude and overlap each other. Compared to general pedestrian detection, there is also more variation in human posture. This paper proposes a real-time human detection network, Dynamic Dual-Peak Network (DDPNet), which specifically addresses human object detection in overlapping and crowded scenes. We design a deep cascade fusion module to enhance the feature extraction capability of the anchor-free model for small objects in crowded scenes. In the meantime, the head-body dual-peak activation module is used to improve the prediction score of the central region of the occluded individual through low occlusion components. By this improvement strategy, the network's ability is enhanced to discriminate individuals in crowded scenes and alleviate the problem caused by individual posture variation. Ultimately, we propose a novel Exhale-Inhale method to adjust the feature mapping ranges for various scale objects dynamically. In the process of ground truth mapping, the overlapping of individual feature information is reduced. Our DDPNet achieves competitive performance on the CrowdHuman dataset and executes real-time inference of almost 3x-7x faster than competitive methods.
机译：由于物体遮挡并相互重叠，人类在拥挤的场景中的人类检测挑战。与一般行人检测相比，人类姿势也有更多的变化。本文提出了一个实时人类检测网络，动态双峰网络（DDPNET），其特异性地解决了重叠和拥挤的场景中的人体对象检测。我们设计深度级联融合模块，以增强无锚模型的特征提取能力，用于在拥挤的场景中的小型物体。同时，通过低闭塞组件，用于改善遮挡个体的中心区域的预测得分。通过这种改进策略，增加了网络的能力，以歧视拥挤的场景中的个人，并减轻各个姿势变化引起的问题。最终，我们提出了一种新的呼气吸气方法，可以动态地调整各种缩放对象的特征映射范围。在地面真相映射的过程中，减少了各个特征信息的重叠。我们的DDPNET在Crowdhuman DataSet上实现了竞争性能，并且比竞争方法更快地执行近3x-7x的实时推断。

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