Modeling from an Object and Multi-object Tracking System

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

doi:10.1109/gscit.2016.20

Cited by 2 publications

(7 citation statements)

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“…In the case of tracking an object in motion, the Kalman filter allows us to estimate the states of motion of the object. Many authors have studied the Kalman filter in object tracking [1,8,9], the differences of the present work and the earlier works are the type and the method of objects tracking.…”

Section: Role Of the Kalman Filter In The Tracking Applicationmentioning

confidence: 93%

“…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%

“…These parameters represent the state vector and the filter measurement vector from Kalman filter. From the works that are studied in the literature [1,4,5], we chose the Kalman filter for estimating the tracking parameters. Figure 4 shows the Kalman filter cycle [8][9][10].…”

Section: Different Function Of the Kalman Filtermentioning

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%

See 4 more Smart Citations

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: Role Of the Kalman Filter In The Tracking Applicationmentioning

confidence: 93%

Section: What Representation Is Appropriate For Tracking Objects?mentioning

confidence: 99%