Aiming at problem that the input of the traditional convolutional neural network（ CNN） is original images with redundant information

which can’t describe the local edge and texture characteristics obviously

a multi-channel sparse CNN（ MCS—CNN） detection algorithm is proposed. Histogram of oriented gradient（ HOG） —feature map and YUV color space are formed into three channels

and the features are extracted from convolutional layer

the features are sparsed by sparse Auto Encoder. The softmax classifier is used to detect pedestrian. In addition to taking full advantage of pixel level feature

the model also fuses the advantages of HOG with pedestrian profile description. Experimental result shows that the accuracy and detecting speed of MCS—CNN model is obvious improved

compared with traditional CNN and HOG—SVM detection algorithms.