基于并行通道级联网络的鲁棒行人检测

Robust Pedestrian Detection Based on Parallel Channel Cascade Network

针对广角视场下远处行人分辨率较低、存在不同程度的畸变的问题,文中提出基于并行通道级联网络的鲁棒行人检测算法.以更快的区域卷积神经网络(Faster RCNN)为基础,引入差分图作为弱监督信息,再引入基于通道级联网络(CCN).然后设计并行CCN,将差分图和原图同时作为并行网络输入,融合更丰富的图像特征.最后在候选区域建议网络中,结合行人尺度在图像中分布的特点,聚类确定符合行人特点的搜索框.实验表明,文中算法在广角视场存在畸变情况下更有利于小尺寸行人检测.

In the wide-angle field with perspective distortion, the resolution of distant pedestrian is low and there is distortion in a broad range of scales. Aiming at these problems, a robust pedestrian detection algorithm based on parallel channel cascade network is proposed. Firstly, differential information is introduced as weak supervisory information. Secondly, a new feature extraction network, channel cascade network(CCN), is proposed. On this basis, a parallel CCN is designed, and the difference map and the original map are utilized as its input. More abundant image features are fused. Finally, in the region proposal network, the distribution of pedestrians in the picture is characterized by clustering, and anchors meeting the pedestrian's characteristics are clustered. Experimental results show that the proposed algorithm is better than the standard Faster-RCNN algorithm and FPN algorithm for small-scale pedestrian detection in the presence of wide-angle field of view distortion.