The invention relates to an image block
deep learning characteristic based
infrared pedestrian detection method, and belongs to the technical fields of
image processing a
computer vision. According to the method, a
data set is divided into a
training set and a
test set. In a training stage, firstly, small image blocks are extracted in a sliding manner on positive and negative samples of the
infrared pedestrian data set, clustering is carried out, and one
convolutional neural network is trained for each type of image blocks; and then
feature extraction is carried out on the positive and negative samples by using the trained
convolutional neural network group, and an
SVM classifier is trained. In a test stage, firstly, a region-of interest is extracted for a test image, then
feature extraction is carried out on the region-of-interest by using the trained
convolutional neural network group, and finally prediction is performed by using the
SVM classifier. The
infrared pedestrian detection method achieves a purpose of
pedestrian detection via a mode of detecting whether each region-of-interest belongs to a pedestrian region or not, so that pedestrians in an
infrared image can be detected accurately under the conditions such that the detection scene is complicated, the environment temperature is high, and the pedestrians vary greatly in scale attitude, and the method provides support for research in follow-up related fields such as intelligent video.