Rotation Adaptive Visual Object Tracking with Motion Consistency

TL;DR

This paper explores how incorporating rotation adaptiveness and motion consistency into visual object tracking improves accuracy and robustness, especially in challenging sequences, by leveraging deep neural networks.

Contribution

It introduces a novel approach that emphasizes rotation adaptiveness and physical motion constraints, enhancing tracking performance over existing methods.

Findings

Rotation adaptiveness improves tracking accuracy.

Motion consistency enhances robustness in challenging scenarios.

The proposed method outperforms current state-of-the-art trackers.

Abstract

Visual Object tracking research has undergone significant improvement in the past few years. The emergence of tracking by detection approach in tracking paradigm has been quite successful in many ways. Recently, deep convolutional neural networks have been extensively used in most successful trackers. Yet, the standard approach has been based on correlation or feature selection with minimal consideration given to motion consistency. Thus, there is still a need to capture various physical constraints through motion consistency which will improve accuracy, robustness and more importantly rotation adaptiveness. Therefore, one of the major aspects of this paper is to investigate the outcome of rotation adaptiveness in visual object tracking. Among other key contributions, the paper also includes various consistencies that turn out to be extremely effective in numerous challenging sequences than the current state-of-the-art.