基于改进R-FCN的多遮挡行人实时检测算法

Multi-occluded pedestrian real-time detection algorithm based on preprocessing R-FCN

当前车辆辅助驾驶系统的一个主要挑战就是在复杂场景下实时检测出多遮挡的行人,以减少交通事故的发生。为了提高系统的检测精度和速度,提出了一种基于改进区域全卷积网络(R-FCN)的多遮挡行人实时检测算法。在R-FCN网络基础上,引进感兴趣区域(RoI)对齐层,解决特征图与原始图像上的RoI不对准问题;改进可分离卷积层,降低R-FCN的位置敏感分数图维度,提高检测速度。针对行人遮挡问题,提出多尺度上下文算法,采用局部竞争机制进行自适应上下文尺度选择;针对遮挡部位可见度低,引进可形变RoI池化层,扩大对身体部位的池化面积。最后为了减少视频序列中行人的冗余信息,使用序列非极大值抑制算法代替传统的非极大值抑制算法。检测算法在基准数据集Caltech训练检测和ETH上产生较低的检测误差,优于当前数据集中检测算法的精度,且适用于检测遮挡的行人。

One of main challenges of driver assistance systems is to detect multi-occluded pedestrians in real-time in complicated scenes,to reduce the number of traffic accidents.In order to improve the accuracy and speed of detection system,we proposed a real-time multi-occluded pedestrian detection algorithm based on R-FCN.RoI Align layer was introduced to solve misalignments between the feature map and RoI of original images.A separable convolution was optimized to reduce the dimensions of position-sensitive score maps,to improve the detection speed.For occluded pedestrians,a multi-scale context algorithm is proposed,which adopt a local competition mechanism for adaptive context scale selection.For low visibility of the body occlusion,deformable RoI pooling layers were introduced to expand the pooled area of the body model.Finally,in order to reduce redundant information in the video sequence,Seq-NMS algorithm is used to replace traditional NMS algorithm.The experiments have shown that there is low detection error on the datasets Caltech and ETH,the accuracy of our algorithm is better than that of the detection algorithms in the sets,works particularly well with occluded pedestrians.