Abstract
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.
Highlights
The computer vision, from the technological evolution point of view, is the most useful in our days
The experimental results obtained indicate that our algorithm (Camshift and the Kalman filter) gives superior results, in terms of precision, reliability and execution time, in comparison with the various methods presented in the literature (for example the KLT (Kanade Lucas Tomasi) algorithm and the classifier algorithm (Adaboost and SVM) [1–3, 7])
Computer vision with a Human Interface Machine “HIM” is an issue actively studied in many domains, especially since the prices of acquisition and processing equipment have become more attractive
Summary
The computer vision, from the technological evolution point of view, is the most useful in our days It is a discipline at the border of computer science, mathematics, physics, neuroscience and various other disciplines, which aims to initiate the specific issues of image and video analysis from 2D and 3D environment, and to implement a simple object tracking application. This phenomenon provokes a spectacular development of applications in various fields in many sectors of activity: imaging systems, robotics, surveillance systems, identification of interest (automatic annotation and retrieval of video from databases multimedia data), indexing and augmented reality, HMI interaction (gesture and gaze recognition for data entry on computers), etc. We will compare the tracking results for the different video sequences analyzed and show the performance of the implemented algorithm
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