Proceedings ArticleDOI
Deep GoogLeNet Features for Visual Object Tracking
P. Aswathy,Siddhartha,Deepak Mishra +2 more
- pp 60-66
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TLDR
This study demonstrates for the first time, the viability of features extracted from deep layers of GoogLeNet CNN architecture for the purpose of object tracking, and integrated Goog LeNet features in a discriminative correlation filter based tracking framework.Abstract:
Convolutional Neural Network (CNN) has recently become very popular in visual object tracking due to their strong feature representation capabilities. Almost all of the CNN based trackers currently use the features extracted from shallow convolutional layers of VGGNet architecture. This paper presents an investigation of the impact of deep convolutional layer features in an object tracking framework. In this study, we demonstrate for the first time, the viability of features extracted from deep layers of GoogLeNet CNN architecture for the purpose of object tracking. We integrated GoogLeNet features in a discriminative correlation filter based tracking framework. Our experimental results show that the GoogLeNet features provides significant computational advantages over the conventionally used VGGNet features, without much compromise on the tracking performance. It was observed that features obtained from inception modules of GoogLeNet have high depths. Further, Principal Component Analysis (PCA) was employed to reduce the dimensionality of the extracted features. This greatly reduces the computational cost and thus improve the speed of the tracking process. Extensive evaluation have been performed on three benchmark datasets: OTB, ALOV300++ and VOT2016 datasets and its performances are measured in terms of metrics like F-score, One Pass Evaluation, robustness and accuracy.read more
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References
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Proceedings ArticleDOI
Accurate scale estimation for robust visual tracking
TL;DR: This paper presents a novel approach to robust scale estimation that can handle large scale variations in complex image sequences and shows promising results in terms of accuracy and efficiency.
Posted Content
Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
Hyeonseob Nam,Bohyung Han +1 more
TL;DR: Zhang et al. as discussed by the authors proposed a novel visual tracking algorithm based on the representations from a discriminatively trained Convolutional Neural Network (CNN), which pretrain a CNN using a large set of videos with tracking ground-truths to obtain a generic target representation.
Proceedings ArticleDOI
Hierarchical Convolutional Features for Visual Tracking
TL;DR: This paper adaptively learn correlation filters on each convolutional layer to encode the target appearance and hierarchically infer the maximum response of each layer to locate targets.
Proceedings ArticleDOI
Learning Spatially Regularized Correlation Filters for Visual Tracking
TL;DR: The proposed SRDCF formulation allows the correlation filters to be learned on a significantly larger set of negative training samples, without corrupting the positive samples, and an optimization strategy is proposed, based on the iterative Gauss-Seidel method, for efficient online learning.
Journal ArticleDOI
Visual Tracking: An Experimental Survey
Arnold W. M. Smeulders,Dung M. Chu,Rita Cucchiara,Simone Calderara,Afshin Dehghan,Mubarak Shah +5 more
TL;DR: It is demonstrated that trackers can be evaluated objectively by survival curves, Kaplan Meier statistics, and Grubs testing, and it is found that in the evaluation practice the F-score is as effective as the object tracking accuracy (OTA) score.