Pedestrian detection based on adaptive pooling method

Pedestrian detectors based on convolutional neural networks generally adopt image recognition network, which usually causes the following problems:1) multi-pool layers lead to the loss of feature information of small target pedestrian; 2) the single pool method leads to the weakening or even loss of the local important feature information of pedestrians. Therefore, based on the maximum pooling and average pooling methods, an adaptive pooling method is proposed, and combined with the Faster R-CNN, an effective pedestrian detector is formed, so as to enhance the local important feature information of pedestrians and retain the effective feature information of small target pedestrians. Through a large number of experiments on several public pedestrian datasets, the results show that compared with the traditional convolutional neural network pedestrian detector, the proposed method reduces the miss rate by about 2%~3%, which verifies the effectiveness of the method.