Face detection and tracking system with block-matching, meanshift and camshift algorithms and Kalman filter

Salhi, Afef; Moresly, Yacine; Ghozzi, Fahmi; Fakhfakh, Ahmed

doi:10.1109/sta.2017.8314915

Cited by 10 publications

(9 citation statements)

References 4 publications

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“…In an object tracking algorithm, there are generally four steps: object detection, location, association, and trajectory estimation. We will be interested in this master's work [1,2,7] to study the different methods of representing objects in a video sequence.…”

Section: What Representation Is Appropriate For Tracking Objects?mentioning

confidence: 99%

“…Each limitation must be overcome in order to avoid failure of the tracking algorithm. In an object tracking algorithm, there are generally four steps: detection, location, association, and trajectory estimation [1][2][3]. The algorithms are composed by three important modules: block matching and meanshift, camshift, Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of tracking an object in motion, the Kalman filter allows us to estimate the states of motion of the object (and therefore predetermine the areas of motion in the following frames with using the combination for three algorithms (block-matching, Camshift and Kalman Filter)) and thus adds robustness tracking objects. Many authors have studied the Kalman filter in object tracking [1,2]. In this work, we optimized many criteria in image and video processing application.…”

Section: Introductionmentioning

confidence: 99%

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Estimation for Motion in Tracking and Detection Objects with Kalman Filter

Salhi¹,

Ghozzi²,

Fakhfakh³

2020

Dynamic Data Assimilation - Beating the Uncertainties

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The Kalman filter has long been regarded as the optimal solution to many applications in computer vision for example the tracking objects, prediction and correction tasks. Its use in the analysis of visual motion has been documented frequently, we can use in computer vision and open cv in different applications in reality for example robotics, military image and video, medical applications, security in public and privacy society, etc. In this paper, we investigate the implementation of a Matlab code for a Kalman Filter using three algorithm for tracking and detection objects in video sequences (block-matching (Motion Estimation) and Camshift Meanshift (localization, detection and tracking object)). The Kalman filter is presented in three steps: prediction, estimation (correction) and update. The first step is a prediction for the parameters of the tracking and detection objects. The second step is a correction and estimation of the prediction parameters. The important application in Kalman filter is the localization and tracking mono-objects and multi-objects are given in results. This works presents the extension of an integrated modeling and simulation tool for the tracking and detection objects in computer vision described at different models of algorithms in implementation systems.

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Section: What Representation Is Appropriate For Tracking Objects?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimation for Motion in Tracking and Detection Objects with Kalman Filter

Salhi¹,

Ghozzi²,

Fakhfakh³

2020

Dynamic Data Assimilation - Beating the Uncertainties

Self Cite

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“…The Meanshift algorithm is used to accurately detect the location of target tracking [8] by using thi s method and then make it easy the accuracy of the calculation of the tracking results [9]. The Meanshift algorithm is also used to solve facial-detection and tracking-based systems [10], if an imbalance occurs, it will affect the performance of [11].…”

Section: Introductionmentioning

confidence: 99%

K Means Clustering and Meanshift Analysis for Grouping the Data of Coal Term in Puslitbang tekMIRA

et al. 2018

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Indonesian government agencies under the Ministry of Energy and Mineral Resources have prob lems in classifying data dictionary of coal. This research conduct grouping coal dictionary using K-Means and MeanShift algorithm. K-means algorithm is used to get cluster value on character and word criteria. The last iteration of Euclidian distance calculation data on k-means comb ine with Meanshift algorithm. The meanshift calculates centroid b y selecting different b andwidths. The result of grouping using k-means and meanshift algorithm shows different centroid to find optimum b andwidth value. The data dictionary of this research has sorted in alphab etically.

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“…Different algorithms based on image processing techniques have been applied to detect multiple objects and these frequently used methods include meanshift, optical flow, Kalman, etc [1]. And their new applications to the national level, in the trade, or in militarily are becoming more and more sophisticated.…”

Section: Introductionmentioning

confidence: 99%