In the field of computer vision, object classification and object detection are widely used in many fields. The traditional object detection have two main problems:one is that sliding window of the regional selection strategy is high time complexity and have window redundancy. And the other one is that Robustness of the feature is not well. In order to solve those problems, Regional Proposal Network (RPN) is used to select candidate regions instead of selective search algorithm. Compared with traditional algorithms and selective search algorithms, RPN has higher efficiency and accuracy. We combine HOG feature and convolution neural network (CNN) to extract features. And we use SVM to classify. For TorontoNet, our algorithm's mAP is 1.6 percentage points higher. For OxfordNet, our algorithm's mAP is 1.3 percentage higher.
In computer vision, pedestrian detection is a key problem. In this paper, we propose to speed up the HOG+SVM algorithm without sacrificing the classification accuracy. In order to eliminate the effects of aliasing phenomenon that products in the process of HOG extraction, we used trilinear interpolation to extract feature. This paper proposed HOG pedestrian detection method based on edge symmetry. In these experiments, we used INRIA dataset. Traditional HOG pedestrian detection is presence of slow detection speed and low detection rate. Experiments show that using trilinear interpolation and edge symmetry not only can improve the detection effect, but also can improve the detection rate.