Structured group local sparse tracker

Javanmardi, Mohammadreza; Qi, Xiaojun

doi:10.1049/iet-ipr.2018.6578

Cited by 5 publications

(2 citation statements)

References 68 publications

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“…For the OTB experiments, we compare TMFT with 25 state-of-the-art trackers including DSST [20], KCF [21], TGPR [22], MEEM [23], MUSTer [24], LCT [25], RSST [26], SRDCF [27], SiamFC [28], DeepSRDCF [29], ADNet [30], CFNet [31], SGLST [32], SCT [33], CNN-SVM [2], CCOT [34], ECO [35], MDNet [3], VITAL [11], CREST [36], TRACA [37], SiamRPN [38], STAPLE [39], CNT [40] and HDT [41]. We use two common performance metrics, namely, overlap success and centre error precision, to evaluate the performance of each tracker.…”

Section: Otb Experimentsmentioning

confidence: 99%

Object tracking using temporally matching filters

Robeson

Javanmardi

2021

IET Computer Vision

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One of the primary challenges of visual tracking is the variable appearance of the target object. As tracking proceeds, the target object can change its appearance due to illumination changes, rotations, deformations etc. Modern trackers incorporate online updating to learn how the target changes over time. However, they do not use the history of target appearance. To address this shortcoming, we uniquely use domain adaptation with the target appearance history to efficiently learn a temporally matching filter (TMF) during online updating. This TMF emphasizes the persistent features found in different appearances of the target object. It also improves the classification accuracy of the convolutional neural network by assisting the training of the classification layers without incurring the runtime overhead of updating the convolutional layers. Extensive experimental results demonstrate that the proposed TMF-based tracker, which incorporates domain adaptation with the target appearance history, improves tracking performance on three benchmark video databases (OTB-50, OTB-100 and VOT2016) over other online learning trackers. Specifically, it improves the overlap success of VITAL and MDNet by 0.44 % and 1.03 % on the OTB-100 dataset and improves the accuracy of VITAL and MDNet by 0.55 % and 0.06 % on the VOT2016 dataset, respectively.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Otb Experimentsmentioning

confidence: 99%

Object tracking using temporally matching filters

Robeson

Javanmardi

2021

IET Computer Vision

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“…The preliminary results of this work are presented in Reference [33], which is based on hand-crafted features. We made a number of improvements in the proposed method: (i) STLDF automatically extracts representative local deep features of target candidates using the pre-trained CNN.…”

Section: Introductionmentioning

confidence: 99%

A Robust Structured Tracker Using Local Deep Features

Javanmardi

Farzaneh

2020

Electronics

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Deep features extracted from convolutional neural networks have been recently utilized in visual tracking to obtain a generic and semantic representation of target candidates. In this paper, we propose a robust structured tracker using local deep features (STLDF). This tracker exploits the deep features of local patches inside target candidates and sparsely represents them by a set of templates in the particle filter framework. The proposed STLDF utilizes a new optimization model, which employs a group-sparsity regularization term to adopt local and spatial information of the target candidates and attain the spatial layout structure among them. To solve the optimization model, we propose an efficient and fast numerical algorithm that consists of two subproblems with the close-form solutions. Different evaluations in terms of success and precision on the benchmarks of challenging image sequences (e.g., OTB50 and OTB100) demonstrate the superior performance of the STLDF against several state-of-the-art trackers.

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