Research On Detection And Recognition Algorithm Of Moving Object In Dynamic Scenes

The methods of moving target detection and recognition is the basis and key in modern intelligent video surveillance systems.It integrates advanced technologies in image processing,pattern recognition,artificial intelligence and other field,it has always been a research hotspot in the field of computer vision.Therefore,it is of great significance to study the motion target detection and recognition algorithm.In the actual situation,there are noise interference,light mutation and object shielding problems in the target scene.At present,no algorithm can simultaneously meet the requirements of high accuracy and good real-time performance and adapt to the detection and recognition of moving objects in all scenarios.In this paper,a motion target detection algorithm based on the extraction of visual background is studied in depth after the discussion on the advantages and disadvantages of other methods,and combined with the idea of W4 method modeling and mean value modeling,a new improved motion target detection method is proposed in this paper.And according to the theory of machine learning and pattern recognition,the method of using SVM to get the classifier of HOG feature training was combined with the moving target detection algorithm to form a set of algorithms for the integration of moving target detection and recognition,and the successful identification of vehicles and pedestrian.The intelligent video surveillance analysis applied to the intersection of trams has achieved a better effect.For the problem of slower processing of video images with larger resolution,a compression algorithm was added to compress the image to increase the algorithm rate.The validity and real-time performance of the algorithm has been verified through experiments.