Understanding Tracking Methodology of Kernelized Correlation Filter

Yadav, Srishti; Payandeh, Shahram

doi:10.1109/iemcon.2018.8614990

Cited by 10 publications

(2 citation statements)

References 19 publications

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“…There are shown in many situations where only RGB information fails to address accurate tracking in the state-of-the-art algorithms [ 16 , 17 ]. One way to overcome RGB flaws is to complement it with depth for improved accuracy and robustness.…”

Section: Related Workmentioning

confidence: 99%

Deep Attention Models for Human Tracking Using RGBD

Rasoulidanesh

Yadav

Herath

et al. 2019

Sensors

Self Cite

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Visual tracking performance has long been limited by the lack of better appearance models. These models fail either where they tend to change rapidly, like in motion-based tracking, or where accurate information of the object may not be available, like in color camouflage (where background and foreground colors are similar). This paper proposes a robust, adaptive appearance model which works accurately in situations of color camouflage, even in the presence of complex natural objects. The proposed model includes depth as an additional feature in a hierarchical modular neural framework for online object tracking. The model adapts to the confusing appearance by identifying the stable property of depth between the target and the surrounding object(s). The depth complements the existing RGB features in scenarios when RGB features fail to adapt, hence becoming unstable over a long duration of time. The parameters of the model are learned efficiently in the Deep network, which consists of three modules: (1) The spatial attention layer, which discards the majority of the background by selecting a region containing the object of interest; (2) the appearance attention layer, which extracts appearance and spatial information about the tracked object; and (3) the state estimation layer, which enables the framework to predict future object appearance and location. Three different models were trained and tested to analyze the effect of depth along with RGB information. Also, a model is proposed to utilize only depth as a standalone input for tracking purposes. The proposed models were also evaluated in real-time using KinectV2 and showed very promising results. The results of our proposed network structures and their comparison with the state-of-the-art RGB tracking model demonstrate that adding depth significantly improves the accuracy of tracking in a more challenging environment (i.e., cluttered and camouflaged environments). Furthermore, the results of depth-based models showed that depth data can provide enough information for accurate tracking, even without RGB information.

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Section: Related Workmentioning

confidence: 99%

Deep Attention Models for Human Tracking Using RGBD

Rasoulidanesh

Yadav

Herath

et al. 2019

Sensors

Self Cite

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“…The algorithm uses cyclic offsets to build out the training samples of the classifier, so that the data matrix is transformed into a cyclic matrix, which transforms the solution of the problem to the Fourier transform domain, avoiding the process of matrix inversion and greatly reducing the complexity of the algorithm. Although the speed and accuracy of the KCF tracking algorithm are better [6] , the phenomenon of target following loss still occurs when the target undergoes a serious occlusion situation, and the tracking frame is not adaptive to the target scale.…”

Section: Introductionmentioning

confidence: 99%

Aerial infrared target tracking algorithm based on kernel correlation filtering and SIFT features in complex background

Li,

Lou,

Guo

2023

AOPC 2023: Infrared Devices and Infrared Technology; And Terahertz Technology and Applications

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DATaR: Depth Augmented Target Redetection using Kernelized Correlation Filter

Yadav

Payandeh

2022

Multimedia Systems

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Unlike deep learning which requires large training datasets, correlation filter-based trackers like Kernelized Correlation Filter (KCF) use implicit properties of tracked images (circulant structure) for training in real time. Despite their popularity in tracking applications, there exists significant drawbacks of the tracker in cases like occlusions and out-of-view scenarios. This paper attempts to address some of these drawbacks with a novel RGB-D Kernel Correlation tracker in target re-detection. Our target re-detection framework not only re-detects the target in challenging scenarios but also intelligently adapts to avoid any boundary issues. Our results are experimentally evaluated using (a) standard dataset and (b) real time using the Microsoft Kinect V2 sensor. We believe this work will set the basis for improvement in the effectiveness of kernel-based correlation filter trackers and will further the development of a more robust tracker.

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Understanding Tracking Methodology of Kernelized Correlation Filter

Cited by 10 publications

References 19 publications

Deep Attention Models for Human Tracking Using RGBD

Deep Attention Models for Human Tracking Using RGBD

Aerial infrared target tracking algorithm based on kernel correlation filtering and SIFT features in complex background

DATaR: Depth Augmented Target Redetection using Kernelized Correlation Filter

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