Abstract
Moving target detection is mostly based on the detection of the underlying video information. This paper discusses the five aspects of target detection concept, YOLOV3 principle introduction, experimental method and process, experimental results and discussion, and YOLOV3 application scenario. Moving target detection is the basis for achieving target recognition tracking. Since the video sequence is composed of a sequence of image frames having a certain continuity in time, the detection of the moving object in the video is performed by extracting the sequence of image frames from the video sequence according to a certain period. We use YOLOV3 for target detection experiments. The first step is to configure the model training environment. The second step is to download and open the YOLOv3 project from Github. In the third step, we use Label Img to label the data. In the fourth step, after the model training is over, we test the trained model.
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