激光与光电子学进展, 2020, 57 (4): 041502, 网络出版: 2020-02-20
基于卷积神经网络的特征融合视频目标跟踪方法 下载: 1486次
Feature Fusion Video Target Tracking Method Based on Convolutional Neural Network
机器视觉 目标跟踪 边缘特征 卷积神经网络 特征融合 machine vision target tracking edge feature convolutional neural network feature fusion
摘要
针对计算机视觉中目标跟踪的问题,提出基于卷积神经网络(CNN)提取深度特征并与边缘特征进行自适应融合的策略来实现视频目标的跟踪算法。卷积神经网络的低层网络可以获取目标的一部分空间结构、形状等特征;高层网络可以获得相对比较抽象的部分语义信息。将VGG16神经网络中第2个卷积层Conv1-2、第4个卷积层Conv2-2和最后一个卷积层Conv5-3提取的深度特征与边缘特征进行特征的自适应融合来实现视频目标跟踪。在OTB100数据集中对本文算法进行实验验证与分析,结果表明,本文算法能够对目标实现更加准确的定位。
Abstract
To solve the target tracking problem in computer vision, this study proposes a strategy based on a convolutional neural network (CNN) that extracts depth features and adaptively blends with edge features to realize the tracking algorithm for video targets. The low-level network of CNN can acquire a part of the spatial structure and shape of the target. High-level network of CNN can obtain relatively abstract partial semantic information. Herein, depth features are extracted by the second convolutional layer Conv1-2, the fourth convolutional layer Conv2-2, and the last convolutional layer Conv5-3 in VGG16 neural network. The above mentioned features are fused with the edge feature adaptively to achieve video object tracking. Herein, the experimental verification and analysis of the proposed method are conducted on the OTB100 dataset. Results show that the proposed method can achieve accurate positioning of the target.
刘美菊, 曹永战, 朱树云, 杨尚奎. 基于卷积神经网络的特征融合视频目标跟踪方法[J]. 激光与光电子学进展, 2020, 57(4): 041502. Meiju Liu, Yongzhan Cao, Shuyun Zhu, Shangkui Yang. Feature Fusion Video Target Tracking Method Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041502.