Rotation Adaptive Visual Object Tracking with Motion Consistency
Litu Rout,Siddhartha,Deepak Mishra,Rama Krishna Sai Subrahmanyam Gorthi +3 more
- pp 1047-1055
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TLDR
In this paper, the authors investigated the outcome of rotation adaptiveness in visual object tracking and also included various consistencies that turn out to be extremely effective in numerous challenging sequences than the current state-of-the-art.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.read more
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References
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Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
TL;DR: In this article, discriminative correlation filters (DCF) have demonstrated excellent performance for visual object tracking, and the key to their success is the ability to efficiently exploit available negative data.
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A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration
Yang Li,Jianke Zhu +1 more
TL;DR: This paper presents a very appealing tracker based on the correlation filter framework and suggests an effective scale adaptive scheme to tackle the problem of the fixed template size in kernel correlation filter tracker.
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Online selection of discriminative tracking features
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Proceedings ArticleDOI
Learning Spatially Regularized Correlation Filters for Visual Tracking
TL;DR: In this paper, a spatial regularization component is introduced in the learning to penalize correlation filter coefficients depending on their spatial location, which allows the correlation filters to be learned on a significantly larger set of negative training samples, without corrupting the positive samples.
Proceedings ArticleDOI
Visual Tracking with Fully Convolutional Networks
TL;DR: An in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet shows that the proposed tacker outperforms the state-of-the-art significantly.