Improved object tracking via joint color-LPQ texture histogram based mean shift algorithm

Medouakh, Saadia; Boumehraz, Mohamed; Terki, Nadjiba

doi:10.1007/s11760-017-1196-2

Cited by 15 publications

(6 citation statements)

References 29 publications

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“…The more accurate the boundary of the object, the better it is for tracking. Formula (7) indicates the definition of OR:…”

Section: Comparison With the Original Tldmentioning

confidence: 99%

“…Choosing suitable features to represent the target is of great significance for visual object tracking to face the abovementioned challenging factors. Several authors have reported that color can adapt to plastic deformation well [7]. However, it is not easy for color to discriminate an object from the background [8].…”

Section: Introductionmentioning

confidence: 99%

“…For example, mean shift is a classical tracking algorithm. On the basis of the mean shift iterations, the most probable location of an object in a frame is found [7]. Particle-filtering-based trackers establish a certain model for objects and determine an object's location by calculating the similarity between the particles and the target [12].…”

Section: Introductionmentioning

confidence: 99%

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A Visual Object Tracking Algorithm Based on Improved TLD

et al. 2020

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Visual object tracking is an important research topic in the field of computer vision. Tracking–learning–detection (TLD) decomposes the tracking problem into three modules—tracking, learning, and detection—which provides effective ideas for solving the tracking problem. In order to improve the tracking performance of the TLD tracker, three improvements are proposed in this paper. The built-in tracking module is replaced with a kernelized correlation filter (KCF) algorithm based on the histogram of oriented gradient (HOG) descriptor in the tracking module. Failure detection is added for the response of KCF to identify whether KCF loses the target. A more specific detection area of the detection module is obtained through the estimated location provided by the tracking module. With the above operations, the scanning area of object detection is reduced, and a full frame search is required in the detection module if objects fails to be tracked in the tracking module. Comparative experiments were conducted on the object tracking benchmark (OTB) and the results showed that the tracking speed and accuracy was improved. Further, the TLD tracker performed better in different challenging scenarios with the proposed method, such as motion blur, occlusion, and environmental changes. Moreover, the improved TLD achieved outstanding tracking performance compared with common tracking algorithms.

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“…The more accurate the boundary of the object, the better it is for tracking. Formula (7) indicates the definition of OR:…”

Section: Comparison With the Original Tldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Visual Object Tracking Algorithm Based on Improved TLD

et al. 2020

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“…It is device dependent and it is not a perceptual model [12]. However, due to the high correlation between the three components red, green and blue, it is considered more beneficial working in the HSV space because it is a more intuitive and user oriented color space [15,17,21]. Therefore, the use of the HSV space is convenient when color characterization by only one dimension is desired.…”

Section: Color Modelmentioning

confidence: 99%

Intuitionistic fuzzy set and fuzzy mathematical morphology applied to color leukocytes segmentation

Bouchet

Montes

Ballarin

et al. 2019

SIViP

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This work presents a new algorithm based on Atanassov's intuitionistic fuzzy sets and Fuzzy Mathematical Morphology to Leukocytes segmentation in color images. The main idea is based on modeling a color image as an Atanassov's intuitionistic fuzzy set using the hue component in the HSV color space. Then, a pixel labelled as leukocyte is selected and compared to the whole image with a similarity measure. Thus, the leukocyte is segmented and separated from the rest of the image. The experimental results show that the algorithm have a good performance, reaching a value of 99.41% for the correct classification of Leukocytes and a 99.23% for the correct classification of the background. Other metrics such as accuracy, precision and recall have been calculated obtaining 99.32%, 99.41% and 99.24%, respectively. The algorithm presents two important characteristics: It works directly over the color images without the need of converting the image in gray scale and it does not produce false colors because as Fuzzy Morphological operators guarantees it.

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“…For example, lesions in endoscopic videos or people in surveillance recordings can be recognized in single pictures, but their visibility can be expected to last for a number of consecutive frames. The continuity of videos is implicitly the basis of object tracking methods which adaptively adjust the representations of tracked objects [3,4] or exploit their inter-frame similarity [5]. Even though an observed item can change dramatically over time, all changes are assumed to be gradual and traceable.…”

Section: Introductionmentioning

confidence: 99%

Systematic approach to binary classification of images in video streams using shifting time windows

Blokus

Krawczyk

2018

SIViP

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Multiple algorithms classifying frames in video sequences consider them only as separate images. After pointing out the properties of real-life recordings and classifications of their frames, we propose a new shifting time window approach for improving binary classifications. It proceeds in two steps: First, well-known classification algorithms are used separately for each frame to acquire preliminary classifications. Secondly, the results of the previous step are analyzed in relatively short sequences of consecutive images (the shifting time window). Taking into account the continuous nature of analyzed real-life videos, the preliminary binary classification sequences can be corrected. In consequence, the classification quality is improved. Furthermore, we offer a systematic approach where all parameters of the proposed algorithm (such as the window length or vote weight distribution in the window) are considered and their optimal values are determined. Experiments on representative examples confirm the advantages of the proposed approach. Keywords Image classification in videos • Binary classifications • Temporal relations • Continuous properties • OFA and FSA methods

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Improved object tracking via joint color-LPQ texture histogram based mean shift algorithm

Cited by 15 publications

References 29 publications

A Visual Object Tracking Algorithm Based on Improved TLD

A Visual Object Tracking Algorithm Based on Improved TLD

Intuitionistic fuzzy set and fuzzy mathematical morphology applied to color leukocytes segmentation

Systematic approach to binary classification of images in video streams using shifting time windows

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