Object Detection And Identification Method Research Based On Convolution Neural Network

With the development of the era,public safety problem has become one of focus of our attention,and the demand for video surveillance has been rapid grow n.The traditional manual processing of video surveillance methods have been unable to meet our requirements.Depth Learning is one of the most important breakthroughs in the field of artificial intelligence in the past ten years.In recent years,the combination of deep learning and computer vision has made a major breakthrough in the recognition of visual tasks.At the same time,the application prospect of computer vision is very broad,the impact on the future may also be revolutionary,computer vision and deep learning will be the next big event in the information age.Based on the recent years of deep learning and computer vision with the breakthrough work,we use food traceability video as the research starting point,design a method of object detection and recognition in video surveillance based on deep learning.The main jobs are as follows:1.We compare several existing background-based target detection algorithms and proposed a background modified method of target detection based on IIR filter combined with our actual application scenarios.construct an annotated image database and train a convolutional neural network model.We realize the target detection and recognition system and analyz and validate the experimental results.This method is an improvement on the IIR filtering method,which can solve the illumination change and the void caused by the target in the background,so as to quickly and accurately select the images with the moving object from the existing video.2.We build a labeled image database,trained the optimized convolution neural network model.Finally,we combine the target detection method with the trained convolutional neural network model for real-time video detection,and analyz and validate the experimental results.