Recognition of Bullet Holes Based on Video Image Analysis

Abstract

The technology of computer vision is used in the training of military shooting. In order to overcome the limitation of the bullet holes recognition using Video Image Analysis that exists over-detection or leak-detection, this paper adopts the support vector machine algorithm and convolutional neural network to extract and recognize Bullet Holes in the digital video and compares their performance. It extracts HOG characteristics of bullet holes and train SVM classifier quickly, though the target is under outdoor environment. Experiments show that support vector machine algorithm used in this paper realize a fast and efficient extraction and recognition of bullet holes, improving the efficiency of shooting training.