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

2017 ◽  
Author(s):  
Afef Salhi ◽  
Yacine Moresly ◽  
Fahmi Ghozzi ◽  
Ahmed Fakhfakh
Keyword(s):  
Kalman Filter ◽  
Face Detection ◽  
Tracking System ◽  
Block Matching ◽  
Detection And Tracking

scholarly journals Estimation for Motion in Tracking and Detection Objects with Kalman Filter

2020 ◽  
Author(s):  
Afef Salhi ◽  
Fahmi Ghozzi ◽  
Ahmed Fakhfakh
Keyword(s):  
Computer Vision ◽  
Kalman Filter ◽  
Visual Motion ◽  
Optimal Solution ◽  
Block Matching ◽  
Detection And Tracking ◽  
Tracking Object ◽  
Tracking Objects ◽  
Localization And Tracking ◽  
Prediction Parameters

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.

Detection and Tracking System for Hurdle Skilled Videos

2014 ◽  
Vol 543-547 ◽  
pp. 4677-4680
Author(s):  
Ai Ling Song ◽  
Kai Chen
Keyword(s):  
Kalman Filter ◽  
Probability Distribution ◽  
Tracking System ◽  
Detection And Tracking ◽  
Precise Analysis ◽  
Kalman Filter Algorithm

The precise analysis the details of the action and trajectory in the hurdlers technical videos can provide training deficiencies for the coaches.To realize the analysis of technical videos, we must firstly detect and track the athletes in the video. Based on the Opencv vision library of Microsoft Visual Studio 2008, we solve the problem of background shake by Surendra algorithm. Then the probability distribution of the target pixel to positioning the athletes is calculated using Kalman filter algorithm to predict its trajectory. Finally, we finish the recognition and detection of athletes by a combination of both methods.

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