Research on optimization of video surveillance target detection algorithms based on YOLO,SSD,and Faster R-CNN
As the complexity of video surveillance systems increases,the need for massive real-time and accurate video sur-veillance target detection becomes crucial.Existing video target detection algorithms such as YOLO,SSD,and Faster R-CNN each have their strengths and weaknesses,and none can fully meet the diverse requirements of video detection on their own.In light of this,a hybrid target detection algorithm is proposed for video target detection,which combines the rapid detection capabilities of YOLO,the multi-scale processing advantages of SSD,and the high accuracy features of Faster R-CNN,aimed at optimizing the per-formance of video surveillance.Experimental validation on synthetic and real-world datasets has shown significant improvements in speed and accuracy with the hybrid algorithm,especially in handling small targets and dense traffic scenarios.
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