Tracking Of Multiple Moving Objects Research Articles

Quantized Kalman Filter-Based Pattern Matching for Detection and Tracking of Moving Objects

Detection And Tracking Of Multiple Moving Objects From A Sequence Of Video Frame And Obtaining Visual Records Of Objects Play An Important Role In The Video Surveillance Systems. Transform And Filtering Technique Designed For Video Pattern Matching And Moving Object Detection, Failed To Handle Large Number Of Objects In Video Frame And Further Needs To Be Optimized. Several Existing Methods Perform Detection And Tracking Of Moving Objects. However, The Performance Efficiency Of The Existing Methods Needs To Be Optimized To Achieve More Robust And Reliable Detection And Tracking Of Moving Objects. In Order To Improve The Pattern Matching Accuracy, A Quantized Kalman Filter-Based Pattern Matching (Qkf-Pm) Technique Is Proposed For Detecting And Tracking Of Moving Objects. The Present Phase Includes Three Functionalities: Top-Down Approach, Kernel Pattern Segment Function And Kalman Filtering. First, The Top-Down Approach Based On Kalman Filtering (Kf) Technique Is Performed To Detect The Chromatic Shadows Of Objects. Next, Kernel Pattern Segment Function Creates The Seed Points For Detecting Moving Object Pattern. Finally, Object Tracking Is Performed Using The Proposed Quantized Kalman Filter Based On The Center Of Seed Point Affinity Feature Values Are Used To Track The Moving Objects In A Particular Region Using The Minimum Bounding Box Approach. Experimental Results Reveals That The Proposed Qkf-Pm Technique Achieves Better Performance In Terms Of True Detection Rate, Pattern Matching Accuracy, Pattern Matching Time, And Object Tracking Accuracy With Respect To The Number Of Video Frames Per Second.

Joint Initialization and Tracking of Multiple Moving Objects Using Doppler Information

In this correspondence, a new multi-target tracking (MTT) algorithm based on the probability hypothesis density (PHD) filtering framework is designed in order to improve tracking performance via the proposal of two contributions. First, unlike typical existing systems, Doppler information is additionally employed to enhance the clutter rejection capability. Specifically, position and Doppler measurements are iteratively incorporated in a two-step process based on a Gaussian mixture PHD (GMPHD) filter. Second, a concrete initialization process is proposed in the birth intensity design of the GMPHD. The initialization process from consecutive measurements leads to a reliable birth intensity that improves track management performance. Both contributions are subsequently evaluated through MTT simulations, the results of which verify that the proposed algorithm is viable.

Multiple Moving Object Tracking Using Particle Filter with Motion Samples and a Window

Multiple Moving Object Tracking Using Particle Filter with Motion Samples and a Window

Tracking of Multiple Moving Objects in Dynamic Image of Omni-Directional Camera Using PHD Filter

A method of multiple moving objects tracking in dynamic image of omni-directional camera has been proposed. Finite random set (FRS) based state space model is employed in the method due to its inherent nature capable to represent the scene having occlusion and appearance of object as well as missing and false detection in observation. Sequential Monte Carlo (SMC) implementation of Probability hypothesis density (PHD) filter has been used for estimating state of the state space model. The state is a finite random set of single object states, where each element of the set consists of position and velocity of the object in panoramic image coordinate of omni-directional camera image. We propose a new method to display tracking result from weighted particles obtained from the estimation process by SMC implementation of PHD filter. Key idea of the method is to put an integer label on each particle, where the label indicates specific object among multiple objects in the image scene tracked by the particle. Numerical simulation and real image experiments illustrate tracking performance of the proposed method.

Age-Related Changes in Attentional Tracking of Multiple Moving Objects

In a multiple-object tracking (MOT) task, young and older adults attentively tracked a subset of 10 identical, randomly moving disks for several seconds, and then tried to identify those disks that had comprised the subset. Young adults who habitually played video games performed significantly better than those who did not. Compared to young subjects (mean age = 20.6 years) with whom they were matched for video game experience, older subjects (mean age = 75.3 years) showed much reduced ability to track multiple moving objects, particularly with faster movement or longer tracking times. Control measurements with stationary disks show that the age-related decline in MOT was not caused by a general change in memory per se. To generate an item-wise performance measure, we examined older subjects' proportion correct according to the serial order in which individual disks were identified. Correct identification of target disks declined with the order in which targets were reported, suggesting that attentional tracking produced graded, rather than all-or-none, outcomes.

Segmentation and Tracking of Multiple Moving Objects for Intelligent Video Analysis

This paper aims to address two of the key research issues in computer vision — the detection and tracking of multiple objects in the cluttered dynamic scene — that underpin the intelligence aspects of advanced visual surveillance systems aiming at automated visual events detection and behaviour analysis. We discuss two major contributions in resolving these problems within a systematic framework. Firstly, for accurate object detection, an efficient and effective scheme is proposed to remove cast shadows/highlights with error corrections based on a conditional morphological reconstruction. Secondly, for effective tracking, a temporal-template-based tracking scheme is introduced, using multiple descriptive cues (velocity, shape, colour, etc) of the 2-D object appearance together with their respective variances over time. A scaled Euclidean distance is used as the matching metric, and the template is updated using Kalman filters when a matching is found or by linear mean prediction in the case of occlusion. Extensive experiments are carried out on video sequences from various real-world scenarios. The results show very promising tracking performance.