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Open AccessProceedings ArticleDOI

Rotation Adaptive Visual Object Tracking with Motion Consistency

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.

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Citations
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Posted Content

Fast Visual Object Tracking with Rotated Bounding Boxes

TL;DR: A novel algorithm that uses ellipse fitting to estimate the bounding box rotation angle and size with the segmentation(mask) on the target for online and real-time visual object tracking.
Journal ArticleDOI

An adaptive template matching-based single object tracking algorithm with parallel acceleration

TL;DR: This paper proposes an adaptive template matching-based single object tracking algorithm framework to achieve template update online, based on the Faster-RCNN model, and presents a parallel strategy to accelerate the process of template matching.
Book ChapterDOI

WAEF: Weighted Aggregation with Enhancement Filter for Visual Object Tracking

TL;DR: This paper proposes a different approach to regress in the temporal domain, based on weighted aggregation of distinctive visual features and feature prioritization with entropy estimation in a recursive fashion, and provides a statistics based ensembler approach for integrating the conventionally driven spatial regression results and the proposed temporal regression results to accomplish better tracking.
Journal ArticleDOI

RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking

TL;DR: A novel rotation adaptive tracker with motion constraint (RAMC) is proposed to explore how the hybridization of angle and motion information can be utilized to boost SV object tracking from two branches: rotation and translation.
Book ChapterDOI

Learning Rotation Adaptive Correlation Filters in Robust Visual Object Tracking

TL;DR: A robust framework is proposed that offers the provision to incorporate illumination and rotation invariance in the standard Discriminative Correlation Filter (DCF) formulation and supervise the detection stage of DCF trackers by eliminating false positives in the convolution response map.
References
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Siamese Neural Networks for One-shot Image Recognition

TL;DR: A method for learning siamese neural networks which employ a unique structure to naturally rank similarity between inputs and is able to achieve strong results which exceed those of other deep learning models with near state-of-the-art performance on one-shot classification tasks.
Proceedings ArticleDOI

Learning Multi-domain Convolutional Neural Networks for Visual Tracking

TL;DR: A novel visual tracking algorithm based on the representations from a discriminatively trained Convolutional Neural Network using a large set of videos with tracking ground-truths to obtain a generic target representation.
Journal ArticleDOI

Struck: Structured Output Tracking with Kernels

TL;DR: A framework for adaptive visual object tracking based on structured output prediction that is able to outperform state-of-the-art trackers on various benchmark videos and can easily incorporate additional features and kernels into the framework, which results in increased tracking performance.
Proceedings ArticleDOI

Learning to compare image patches via convolutional neural networks

TL;DR: This paper shows how to learn directly from image data a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems, and opts for a CNN-based model that is trained to account for a wide variety of changes in image appearance.
Proceedings ArticleDOI

End-to-End Representation Learning for Correlation Filter Based Tracking

TL;DR: In this paper, the Correlation Filter learner is interpreted as a differentiable layer in a deep neural network, which enables learning deep features that are tightly coupled to the correlation filter.
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