现代电子技术2024，Vol.47Issue(20)：118-122,5.DOI:10.16652/j.issn.1004-373x.2024.20.018
基于改进神经网络的视频序列运动目标识别方法
Method of video sequence motion target recognition based on improved neural network
摘要
Abstract
A method of video sequence motion target recognition based on improved neural network is proposed to improve the motion target detection and reduce target missed-detection rate.An improved YOLOv3 motion target recognition model is constructed,which takes different frame video images as model input.After preliminary feature extraction by convolutional layers,the model is input into the deep network composed of 5 residual modules.The feature pyramid is constructed by means of the above sampling method to capture the four scale features of the motion target.At each layer of the feature pyramid,K-means algorithm is used to cluster the real boxes of the motion target,ensuring that the size and proportion of the candidate boxes match the statistical characteristics of the real motion target.The obtained candidate boxes and classifiers are used to detect targets at each position on the feature map.The non-maximum suppression technology is used to remove overlapping boxes,and the repulsive loss function is introduced into the total loss of network training to make the predicted boxes infinitely close to the real boxes of the motion target,so as to realize the precise recognition of sports goals.The experimental results show that the proposed method has significant ability in motion target recognition,when the number of clusters is 12,the AUC and F1 score indicators for motion target recognition can reach 0.92 and 0.90,and the computation is less.关键词
视频序列/运动目标识别/改进YOLOv3网络/特征金字塔/K-means算法/候选框聚类Key words
video sequence/motion target recognition/improved YOLOv3 network/feature pyramid/K-means algorithm/candidate box clustering分类
信息技术与安全科学引用本文复制引用
范建伟,李琳,靳志鑫..基于改进神经网络的视频序列运动目标识别方法[J].现代电子技术,2024,47(20):118-122,5.
