Long-term target tracking combined with re-detection

Juanjuan Wang,Dapeng Oliver Wu,Chengqin Wu,Zengshun Zhao,Haoran Yang,Jixiang Zhang,Ning Xu

doi:10.1186/s13634-020-00713-3

Abstract

Long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art learning adaptive discriminative correlation filters (LADCF) tracking algorithm with a re-detection component based on the support vector machine (SVM) model. The LADCF tracking algorithm localizes the target in each frame, and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to-correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

Highlights

While visual object tracking as a hot research topic in computer vision has been widely applied in various fields, many challenges are still not resolved especially in target disappearance, partial occlusion, and background clutter, and studying a general and powerful tracking algorithm is a tough task.A typical scenario of visual tracking is to track an unknown object in subsequent image frames by giving the initial state of a target in the first frame of the video
The regularization parameters λ1 and λ2 are set to 1 and 15, respectively; the initial penalty parameter μ = 1; the maximum penalty parameter μmax = 20; the maximum number of iterations K = 2; the padding parameter as ρ = 4; the scale factor as a = 1.01; the threshold for re-detection is set to tr = 0.13; and the update threshold is set to tu = 0.20
4.2 Experimental datasets and evaluation criteria The OTB-2015 dataset has a total of 100 video sequences, including 11 challenges, namely, illumination variation (IV), scale variation (SV), occlusion (OCC), deformation (DEF), motion blur (MB), fast motion (FM), in-plane rotation (IPR), out-of-plane rotation (OPR), out-of-view (OV), background clutter (BC), and low resolution (LR)

Summary

Introduction

While visual object tracking as a hot research topic in computer vision has been widely applied in various fields, many challenges are still not resolved especially in target disappearance, partial occlusion, and background clutter, and studying a general and powerful tracking algorithm is a tough task.A typical scenario of visual tracking is to track an unknown object in subsequent image frames by giving the initial state of a target in the first frame of the video. In the past few decades, visual object tracking technology has made significant progress [1,2,3,4,5,6,7,8,9,10]. These methods are very effective for short-term tracking tasks, which the tracked object is almost always in the field of view. During the period of time, the tracking output is wrong in the absence of the target objects. It is important to long-term trackers to determine whether the target is absent and have the capability of re-detection

Methods

Results

Conclusion