Car Recognition Based on HOG Feature and SVM Classifier

Public places such as parks, pedestrian lanes, and dangerous construction sites are often prohibited for vehicles to enter, but there are still some cars that cause hidden safety hazards due to driver negligence or deliberate entry. At the same time, there are problems of high cost and low efficiency in car detection and warning in public places completely relying on manpower. Therefore, in view of the security risks caused by blind car driving in some public places, this paper builds a car recognition model based on directional gradient histogram (HOG) and support vector machine (SVM) to realize the car recognition function. This model is used to judge whether there is a car entering the recognition range, and is applied to the car recognition in a specific public environment, so as to warn cars entering the forbidden area and feed back to the staff for corresponding treatment. Through the experiment and the analysis of sample data confusion matrix, the accuracy and specificity of the model are up to 92.7% and 92.6%, respectively, which proves that the model has a high recognition rate and can be well applied to the automobile recognition task.