Long-term real time object tracking based on multi-scale local correlation filtering and global re-detection

Qi Zhao,Boxue Zhang,Hong Zhang,Zhiying Du,Wenquan Feng,Daniel Sun

doi:10.1007/s00607-020-00807-8

Qi Zhao, Boxue Zhang + Show 4 more

Open Access

https://doi.org/10.1007/s00607-020-00807-8

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Abstract

This paper investigates long-term visual object tracking which is a complex problem in computer vision community and big data analysis, due to the variation of the target and the surrounding environment. A novel tracking algorithm based on local correlation filtering and global keypoint matching is proposed to solve problems occurred during long-term tracking such as occlusion, target-losing, etc. The algorithm consists of two major components: (1) local object tracking module, and (2) global losing re-detection module. The local tracking module optimizes the conventional correlation filtering algorithm. Firstly, the Color Name feature is applied to increase the color sensitivity. Secondly, a scale traversal is employed to accommodate target scale changes. In the global losing re-detection module, the target losing judgment and global re-detection is realized by keypoint feature models of foreground and background. The proposed tracker achieves the 1st place in the VTB50 test set with 81.3% precision and 61.3% success rate, which outperforms other existing state-of-the-art trackers by over 10%. And it achieves the 2nd place in our Chasing-Car test set with a higher real-time performance 43.2 fps. The experimental results show that the proposed tracker has higher accuracy and robustness when dealing with situations like object deformation, occlusion and target-losing, etc.

Highlights

Visual object tracking, the problem of locating objects in a video sequence, is one of the central research topics in the field of computer vision [1] and Big Data Analytics (BDA)
As for the occlusion and out-of-view tests, the success rates of other methods decreased a lot compared to the overall success rate, while our algorithm still maintains a high success rate
It can conclude that the proposed tracker has an excellent performance in dealing with occlusion and target losing compared with other state-of-art trackers

Summary

Introduction

The problem of locating objects in a video sequence, is one of the central research topics in the field of computer vision [1] and Big Data Analytics (BDA). More and more modern applications, such as precision guidance [2], intelligent surveillance [3], and auto-control system [4], etc., require object tracking result of high accuracy. According to the target changes in the process of tracking, object tracking can be divided into long-term tracking and short-term tracking. Most existing trackers focus on short-term tracking and has achieved excellent performance. The long-term tracking problem is still not a fully studied problem [5]. The following problems have been long-standing: First, the model update drift, which is caused by the changes of the appearance or scale of target. The tracker must be simple and flexible enough, as most applications of target tracking always have very high real-time requirements

Methods

Results

Conclusion