Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (8): 2247-2251.DOI: 10.11772/j.issn.1001-9081.2018122593

• Artificial intelligence • Previous Articles     Next Articles

Object tracking algorithm combining re-detection mechanism and convolutional regression network

JIA Yongchao, HE Xiaowei, ZHENG Zhonglong   

  1. College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua Zhejiang 321004, China
  • Received:2019-01-02 Revised:2019-03-08 Online:2019-03-28 Published:2019-08-10
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61572023, 61672467).


贾永超, 何小卫, 郑忠龙   

  1. 浙江师范大学 数学与计算机科学学院, 浙江 金华 321004
  • 通讯作者: 何小卫
  • 作者简介:贾永超(1989-),男,河北迁安人,硕士研究生,主要研究方向:人工智能、目标跟踪;何小卫(1968-),男,浙江金华人,教授,硕士,主要研究方向:图像视频处理、机器学习、区块链;郑忠龙(1975-),男,河北石家庄人,教授,博士生导师,博士,主要研究方向:机器学习、模式识别、图像处理、区块链。
  • 基金资助:

Abstract: Concerning the problem that Context-Ware Correlation Filter (CACF) algorithm based on artificial features has poor tracking performance under the situations of deformation, motion blur and low resolution and when the tracker encounters conditions like severe occlusion, it is easy to fall into local optimum and cause tracking failure, a new object tracking algorithm combining re-detection mechanism and Convolutional Regression Network (CRN) was proposed. In the training phase, the correlation filter was integrated into the deep neural network as a CRN layer, so that the network became a whole for end-to-end training. In the tracking phase, different network layers and their response values were merged through residual connections. At the same time, a re-detection mechanism was introduced to make the tracking algorithm recover from the potential tracking failure, and the re-detector would be activated when the response value was lower than the given threshold. Experimental results on the dataset OTB-2013 show that the proposed algorithm achieves 88.1% accuracy on 50 video sequences, which is 9.7 percentage points higher than the accuracy of original CACF algorithm, and has better results compared with original algorithm on video sequences with attributes like deformation and motion blur.

Key words: object tracking, correlation filter, Convolution Regression Network (CRN), end-to-end, re-detection

摘要: 针对基于人工特征的背景感知相关滤波(CACF)算法在形变、运动模糊、低分辨率情形跟踪效果较差以及跟踪器遇到严重遮挡等情形容易陷入局部最优而导致跟踪失败的问题,提出一种融合重检测机制的卷积回归网络(CRN)目标跟踪算法。在训练阶段,将相关滤波作为CRN层融入进深度神经网络,使网络成为一个整体进行端到端训练;在跟踪阶段,通过残差连接融合不同网络层及其响应值,同时引入重检测机制使算法从潜在的跟踪失败中恢复,当响应值低于给定阈值时激活检测器。在数据集OTB-2013上的实验表明,所提算法在50个视频序列上精确度达到88.1%,相比原始CACF算法提高9.7个百分点,在具有形变、运动模糊等属性的视频序列上相比原始算法表现更优秀。

关键词: 目标跟踪, 相关滤波, 卷积回归网络, 端到端, 重检测

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