A Tracking-Learning-Detection (TLD) method with local binary pattern improved

Jia, Chun-Xiao; Wang, Zhongli; Wang, Xian; Cai, Bin; Huang, Zhenhui; Wang, Guiguo; Zhang, Tianbai; Tong, Dezhong

doi:10.1109/robio.2015.7419004

Cited by 20 publications

(6 citation statements)

References 10 publications

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“…The performance is evaluated using P, R and F. P represents the number of true positives divided by the number of all responses and R represents the number of true positives divided by the number of object occurrences that should have been detected. F combines these two measures as PR / ) R P ( + 2 [16]. A detection whose overlap with ground truth bounding box is larger than 50% will be considered as correct.…”

Section: Methodsmentioning

confidence: 99%

A Robust Multi-Target Visual Tracking Method using TLD Framework

Huang¹,

Yang²

2017

dtcse

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Abstract. This paper investigates a robust multi-target visual tracking method based on TLD framework. We propose a novel visual tracking method that divides the long-term tracking task into four parts: tracker module, learning module, weak-detector module and strong-detector module. Before the tracking, it is essential to sample some basic posture of targets and build several patterns by training. Then, a separate process is allocated for each target may appear in the video or not. The strong-detector module makes a global scanning on every frame. Once a target of interest is detected, the tracker module starts to follow it from frame to frame. The weak-detector module makes a local scanning around the position predicted by the tracker module. The learning module estimates the errors of weak-detector module and updates the weak-detector module to avoid these errors from coming up again. At the last, when the posture of some target has large change, it may result in the metal failure of tracker module and weak-detector module. At this time, the strong-detector module will relocate the target and initialize the others modules.

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Section: Methodsmentioning

confidence: 99%

A Robust Multi-Target Visual Tracking Method using TLD Framework

Huang¹,

Yang²

2017

dtcse

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“…Many breakthroughs have been made and several generative methods have been utilized in MOT, including mean shift, sparse coding, dictionary learning, particle filter, and sliding window [32][33][34][35][36][37]. On the other hand, compared with generative methods, the discriminative methods regard object tracking as a detection problem, also known as tracking by detection (see [38,39] and the related references). The discriminative methods generally train the classifier in the first frame to separate the object from its surrounding background.…”

Section: Introductionmentioning

confidence: 99%

Moving Object Tracking in Satellite Videos by Kernelized Correlation Filter Based on Color-Name Features and Kalman Prediction

Pei

2022

Wireless Communications and Mobile Computing

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This paper studies moving object tracking in satellite videos. For the satellite videos, the object size in the images may be small, the object may be partly occluded, and the image may contain an area resembling dense objects. To handle the above problems, this paper puts forward a kernelized correlation filter based on the color-name feature and Kalman prediction. The original image is mapped to the color-name feature space so that the tracker can process the image with multichannel color features. The Kalman filter is used to predict the moving object position in the tracking process, and the detection area is determined according to the predicted position. The Kalman filter is updated with the detection results to improve the tracking accuracy. The proposed algorithm is tested on Jilin-1 datasets. Compared with the other seven tracking algorithms, the experiment results show that the proposed algorithm has stronger robustness for several complex situations such as rapid target motion and similar object interference. Besides, it is also shown that the proposed algorithm can prevent the problem of tracking failure when the moving object is partially occluded.

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“…An online SVM re-detector was used to predict the location and retain the tracking [15] in case of tracking failure. The nearest-neighbour classifier is with local binary pattern algorithm ITLD in [16] by considering the distinct texture of targets.…”

Section: Introductionmentioning

confidence: 99%

Moving vehicle tracking based on improved tracking–learning–detection algorithm

Dong

Deng

Tong

et al. 2019

IET Computer Vision

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This study addresses the tracking-learning-detection (TLD) algorithm for long-term single-target tracking of moving vehicle from video streams. The problems leading to tracking failures in existing TLD methods are discovered, and an improved TLD (ITLD) tracking algorithm is proposed which is more robust to object occlusion and illumination variation. A square root cubature Kalman filter (SRCKF) is employed in the tracker of TLD to predict the position of the object when occlusion occurs. Besides, this study introduces fast retina keypoint (FREAK) feature into the tracker to alleviate the instability caused by illumination variation or scale variation. The overlap comparison and the normalised cross-correlation coefficient (NCC) are introduced to the integrator of the TLD to obtain reliable bounding boxes with improved tracking precision. Experiments are conducted to compare the performance of the state-of-the-art trackers and the proposed method, using the object tracking benchmark that includes 50 video sequences (OTB-50) and TLD datasets. The experimental results show that the proposed ITLD outperforms on both tracking accuracy and robustness. The proposed method can track a moving vehicle even when it is temporally totally occluded.

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A Tracking-Learning-Detection (TLD) method with local binary pattern improved

Cited by 20 publications

References 10 publications

A Robust Multi-Target Visual Tracking Method using TLD Framework

A Robust Multi-Target Visual Tracking Method using TLD Framework

Moving Object Tracking in Satellite Videos by Kernelized Correlation Filter Based on Color-Name Features and Kalman Prediction

Moving vehicle tracking based on improved tracking–learning–detection algorithm

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