Robust Visual Tracking Using the Bidirectional Scale Estimation

An Zhiyong,Li Jinjiang,Guan Hao

doi:10.1155/2017/3276103

Abstract

Object tracking with robust scale estimation is a challenging task in computer vision. This paper presents a novel tracking algorithm that learns the translation and scale filters with a complementary scheme. The translation filter is constructed using the ridge regression and multidimensional features. A robust scale filter is constructed by the bidirectional scale estimation, including the forward scale and backward scale. Firstly, we learn the scale filter using the forward tracking information. Then the forward scale and backward scale can be estimated using the respective scale filter. Secondly, a conservative strategy is adopted to compromise the forward and backward scales. Finally, the scale filter is updated based on the final scale estimation. It is effective to update scale filter since the stable scale estimation can improve the performance of scale filter. To reveal the effectiveness of our tracker, experiments are performed on 32 sequences with significant scale variation and on the benchmark dataset with 50 challenging videos. Our results show that the proposed tracker outperforms several state-of-the-art trackers in terms of robustness and accuracy.

Highlights

Visual tracking has drawn significant attentions in computer vision with various applications such as activity analysis, video surveillance, and auto control systems
Many new algorithms [2,3,4,5] based on the minimum output sum of squared error (MOSSE) filters have been proposed in researches in field of object tracking
The proposed approach achieves state-of-the-art performance on both scale variation dataset and the benchmark dataset

Summary

Introduction

Visual tracking has drawn significant attentions in computer vision with various applications such as activity analysis, video surveillance, and auto control systems. Despite significant progress in recent years, it is still difficult due to baffling factors in complicated situations such as scale variations, partial occlusion, background clutter, deformation, and fast motion. Many object tracking algorithms have been proposed. Among those trackers, the tracking-bydetection algorithms have achieved excellent performance by learning a discriminative classifier. The MOSSE tracker is robust to variations in lighting, pose, and nonrigid deformations while running with a speed reaching several hundred frames per second. The MOSSE tracker can be performed efficiently because of using the fast Fourier transform (FFT). Many new algorithms [2,3,4,5] based on the MOSSE filters have been proposed in researches in field of object tracking

Methods

Results

Conclusion