Image processed tracking system of multiple moving objects based on Kalman filter

2002 ◽  
Vol 16 (4) ◽  
pp. 427-435
Author(s):  
Dong-Kyu Kim ◽  
Sang-Bong Kim ◽  
Hak-Kyeong Kim
Keyword(s):  
Kalman Filter ◽  
Moving Objects ◽  
Tracking System

Multiple Object Tracking by Scale Space Representation of Objects, Method of Linear Assignment, and Kalman Filter

2016 ◽  
pp. 236-259
Author(s):  
Kumar S. Ray ◽  
Soma Ghosh ◽  
Kingshuk Chatterjee ◽  
Debayan Ganguly
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Moving Objects ◽  
Tracking System ◽  
Scale Space ◽  
Space Representation ◽  
Occluded Objects ◽  
Video Scene ◽  
Cost Parameters ◽  
Linear Assignment

This chapter presents a multi-object tracking system using scale space representation of objects, the method of linear assignment and Kalman filter. In this chapter basically two very prominent problems of multi object tracking have been resolved; the two prominent problems are (i) irrespective of the size of the objects, tracking all the moving objects simultaneously and (ii) tracking of objects under partial and/or complete occlusion. The primary task of tracking multiple objects is performed by the method of linear assignment for which few cost parameters are computed depending upon the extracted features of moving objects in video scene. In the feature extraction phase scale space representation of objects have been used. Tracking of occluded objects is performed by Kalman filter.

Multiple Object Tracking by Scale Space Representation of Objects, Method of Linear Assignment, and Kalman Filter

2018 ◽  
pp. 800-823
Author(s):  
Kumar S. Ray ◽  
Soma Ghosh ◽  
Kingshuk Chatterjee ◽  
Debayan Ganguly
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Moving Objects ◽  
Tracking System ◽  
Scale Space ◽  
Space Representation ◽  
Occluded Objects ◽  
Video Scene ◽  
Cost Parameters ◽  
Linear Assignment

This chapter presents a multi-object tracking system using scale space representation of objects, the method of linear assignment and Kalman filter. In this chapter basically two very prominent problems of multi object tracking have been resolved; the two prominent problems are (i) irrespective of the size of the objects, tracking all the moving objects simultaneously and (ii) tracking of objects under partial and/or complete occlusion. The primary task of tracking multiple objects is performed by the method of linear assignment for which few cost parameters are computed depending upon the extracted features of moving objects in video scene. In the feature extraction phase scale space representation of objects have been used. Tracking of occluded objects is performed by Kalman filter.

scholarly journals Moving-Object Tracking with In-Vehicle Multi-Laser Range Sensors

2008 ◽  
Vol 20 (3) ◽  
pp. 367-377 ◽  
Author(s):  
Masafumi Hashimoto ◽  
◽  
Yosuke Matsui ◽  
Kazuhiko Takahashi ◽  
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Moving Objects ◽  
Tracking System ◽  
Moving Object ◽  
Experimental Result ◽  
Coordinate Frame ◽  
Laser Range ◽  
Assignment Algorithm ◽  
Moving Object Tracking

This paper presents a method for moving-object tracking with in-vehicle 2D laser range sensor (LRS) in a cluttered environment. A sensing area of one LRS is limited in orientation, and hence the mobile robot is equipped with multi-LRSs for omnidirectional sensing. Since each LRS takes the laser image on its own local coordinate frame, the laser image is mapped onto a reference coordinate frame so that the object tracking can be achieved by cooperation of multi-LRSs. For mapping the coordinate frames of multi-LRSs are calibrated, that is, the relative positions and orientations of the multi-LRSs are estimated. The calibration is based on Kalman filter and chi-hypothesis testing. Moving-object tracking is achieved by two steps: detection and tracking. Each LRS finds moving objects from its own laser image via a heuristic rule and an occupancy grid based method. It tracks the moving objects via Kalman filter and the assignment algorithm based data association. When the moving objects exist in the overlapped sensing areas of the LRSs, these LRSs exchange the tracking data and fuse them in a decentralized manner. A rule based track management is embedded into the tracking system in order to enhance the tracking performance. The experimental result of three walking-people tracking in an indoor environment validates the proposed method.

GPS and GSM Based Tracking System for Objects Moving over Wide Geographical Areas

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Fatima Ameen ◽  
Ziad Mohammed ◽  
Abdulrahman Siddiq
Keyword(s):  
Global Positioning System ◽  
Mobile Communications ◽  
Moving Objects ◽  
Tracking System ◽  
Geographical Area ◽  
Tracking Systems ◽  
Acceptable Result ◽  
Internet Service ◽  
Global Positioning ◽  
Tracking Objects

Tracking systems of moving objects provide a useful means to better control, manage and secure them. Tracking systems are used in different scales of applications such as indoors, outdoors and even used to track vehicles, ships and air planes moving over the globe. This paper presents the design and implementation of a system for tracking objects moving over a wide geographical area. The system depends on the Global Positioning System (GPS) and Global System for Mobile Communications (GSM) technologies without requiring the Internet service. The implemented system uses the freely available GPS service to determine the position of the moving objects. The tests of the implemented system in different regions and conditions show that the maximum uncertainty in the obtained positions is a circle with radius of about 16 m, which is an acceptable result for tracking the movement of objects in wide and open environments.

The Research of Moving Target Detection and Tracking System Based on DM6437

2015 ◽  
Vol 734 ◽  
pp. 203-206
Author(s):  
En Zeng Dong ◽  
Sheng Xu Yan ◽  
Kui Xiang Wei
Keyword(s):  
Target Detection ◽  
Digital Signal Processor ◽  
Moving Objects ◽  
Tracking System ◽  
Digital Signal ◽  
Gaussian Mixture ◽  
Background Model ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection And Tracking

In order to enhance the rapidity and the accuracy of moving target detection and tracking, and improve the speed of the algorithm on the DSP (digital signal processor), an active visual tracking system was designed based on the gaussian mixture background model and Meanshift algorithm on DM6437. The system use the VLIB library developed by TI, and through the method of gaussian mixture background model to detect the moving objects and use the Meanshift tracking algorithm based on color features to track the target in RGB space. Finally, the system is tested on the hardware platform, and the system is verified to be quickness and accuracy.

scholarly journals Measurement Feedback Self-Tuning Weighted Measurement Fusion Kalman Filter for Systems with Correlated Noises

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Xin Wang ◽  
Shu-Li Sun
Keyword(s):  
Kalman Filter ◽  
Stochastic Systems ◽  
Tracking System ◽  
Weighted Least Squares ◽  
Full Rank ◽  
Global Optimality ◽  
Measurement Feedback ◽  
Innovation Model ◽  
Self Tuning ◽  
Noise Statistics

For the linear discrete stochastic systems with multiple sensors and unknown noise statistics, an online estimators of the noise variances and cross-covariances are designed by using measurement feedback, full-rank decomposition, and weighted least squares theory. Further, a self-tuning weighted measurement fusion Kalman filter is presented. The Fadeeva formula is used to establish ARMA innovation model with unknown noise statistics. The sampling correlated function of the stationary and reversible ARMA innovation model is used to identify the noise statistics. It is proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion Kalman filter, which means its asymptotic global optimality. The simulation result of radar-tracking system shows the effectiveness of the presented algorithm.

scholarly journals Dynamic Autonomous Identification and Intelligent Lighting of Moving Objects with Discomfort Glare Limitation

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7243
Author(s):  
Sebastian Słomiński ◽  
Magdalena Sobaszek
Keyword(s):  
Moving Objects ◽  
Tracking System ◽  
Electricity Consumption ◽  
Computation Time ◽  
Projection Image ◽  
Reduce Electricity Consumption ◽  
Smart Control ◽  
Discomfort Glare ◽  
Lighting Systems ◽  
Dynamic Lighting

The importance of reducing discomfort glare during the dynamic development of high luminance LEDs is growing fast. Smart control systems also offer great opportunities to reduce electricity consumption for lighting purposes. Currently, dynamic “intelligent” lighting systems are a rapidly developing field. These systems, consisting of cameras and lighting units, such as moving heads or multimedia projectors, are powerful tools that provide a lot of opportunities. The aim of this research is to demonstrate the possibilities of using the projection light in dynamic lighting systems that enable the reduction of discomfort glare and the light pollution phenomenon. The proposed system allows darkening or reducing the luminance of some sensitive zones, such as the eyes or the head, in real-time. This paper explores the development of the markerless object tracking system. The precise identification of the position and geometry of objects and the human figure is used for dynamic lighting and mapping with any graphic content. Time measurements for downloading the depth maps, as well as for identifying the human body’s position and pose, have been performed. The analyses of the image transformation times have been carried out in relation to the resolution of the images displayed by the projector. The total computation time related to object detection and image display translates directly into the precision of fitting the projection image to a moving object and has been shown.

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