scholarly journals Research on Moving Target Tracking Based on FDRIG Optical Flow

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1122
Author(s):  
Gong ◽  
Wang
Keyword(s):  
Optical Flow ◽  
Target Tracking ◽  
Research Work ◽  
Flow Equation ◽  
Superior Performance ◽  
Moving Target ◽  
Moving Target Tracking ◽  
Motion Sequence ◽  
Tracking Model ◽  
Vehicle Motion

Aiming at the problem of moving target recognition, a moving target tracking model based on FDRIG optical flow is proposed. First, the optical flow equation was analyzed from the theory of optical flow. Then, with the energy functional minimization, the FDRIG optical flow technique was proposed. Taking a road section of a university campus as an experimental section, 30 vehicle motion sequence images were considered as objects to form a vehicle motion sequence image with a complex background. The proposed FDRIG optical flow was used to calculate the vehicle motion optical flow field by the Halcon software. Comparable with the classic Horn and Schunck (HS) and Lucas and Kande (LK) optical flow algorithm, the monitoring results proved that the FDRIG optical flow was highly precise and fast when tracking a moving target. The Ettlinger Tor traffic scene was then taken as the second experimental object; FDRIG optical flow was used to analyze vehicle motion. The superior performance of the FDRIG optical flow was further verified. The whole research work shows that FDRIG optical flow has good performance and speed in tracking moving targets and can be used to monitor complex target motion information in real-time.

Moving Target Tracking Using Sparse Optical Flow Method

2013 ◽  
Vol 718-720 ◽  
pp. 2335-2339
Author(s):  
Tian Ding Chen ◽  
Jian Hu ◽  
Chao Lu ◽  
Zhong Jiao He
Keyword(s):  
Optical Flow ◽  
Target Tracking ◽  
New Method ◽  
Moving Target ◽  
Tracking Accuracy ◽  
Experimental Conditions ◽  
Moving Target Tracking ◽  
Research Production ◽  
Harris Corner ◽  
Comparison Results

Moving target tracking is a hot research spot of computer vision and applied in various fields. In this paper, a new tracking method base on sparse optical flow is put forward. In this method, targets are tracked through calculating the movements of Harris corner points, rather than the movements of all pixel points. Experiments results show that the tracking effect of this new method is pretty good. Tracking accuracy can reach more than 80% in most experimental conditions. And according to other peoples research production, experiments based on dense optical flow are done to compare with the new method proposed in this paper. The comparison results show that the new method has high calculation efficiency. This indicates that the method has feasibility and practical value.

Artificial intelligence based tracking model for functional sports training goals in competitive sports

2020 ◽  
pp. 1-13
Author(s):  
Zhe Zhao ◽  
Xingyu Liu ◽  
Xi She
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Physical Training ◽  
Particle Filtering ◽  
Binary Classification ◽  
Moving Target ◽  
Competitive Sports ◽  
Balance Test ◽  
Moving Target Tracking ◽  
Tracking Model

As an advanced training concept, functional physical training is gradually recognized by top athletes for its high training effect and low sports injury. Functional physical training should gradually develop from elite athletes to grassroots athletes, so as to lay a solid foundation for the development of competitive sports. Because particle filtering is susceptible to external factors in moving target tracking, this paper designs a method for sparse coding using local image blocks of the target, establishes a static “impression” and dynamic model for the appearance of the target. The tracking problem is regarded as a binary classification problem between the foreground target and the background image. During the tracking process, the dual particle filter is implemented to alleviate the tracking drift, so that the algorithm can adaptively capture the changes in the target appearance At the same time, it can reduce the update caused by wrong positioning. The subjects’ FMS test and Y balance test have improved in varying degrees; the pressure distribution of the forefoot, arch, and heel tends to be rationalized, and the ratio of internal and external splayed feet has decreased. Experiments show that this particle filter moving target tracking scheme can adapt to changes in the environment and overcome the inflexibility of the global template when dealing with local changes in the target.

A novel improved CKF with adaptive generation of the cubature points and weights for target tracking

2021 ◽  
Author(s):  
Hongpo Fu ◽  
Yongmei Cheng ◽  
Cheng Cheng
Keyword(s):  
Target Tracking ◽  
Superior Performance ◽  
Inner Product ◽  
Estimation Accuracy ◽  
Cubature Rule ◽  
Cubature Kalman Filter ◽  
One Step ◽  
Tracking Model ◽  
Dynamic Filtering ◽  
High Degree

Abstract In the nonlinear state estimation, the generation method of cubature points and weights of the classical cubature Kalman filter (CKF) limits its estimation accuracy. Inspired by that, in this paper, a novel improved CKF with adaptive generation of the cubature points and weights is proposed. Firstly, to improve the accuracy of classical CKF while considering the calculation efficiency, we introduce a new high-degree cubature rule combining third-order spherical rule and sixth-degree radial rule. Next, in the new cubature rule, a novel method that can generate adaptively cubature points and weights based on the distance between the points and center point in the sense of the inner product is designed. We use the cosine similarity to quantify the distance. Then, based on that, a novel high-degree CKF is derived that use much fewer points than other high-degree CKF. In the proposed filter, based on the actual dynamic filtering process, the simultaneously adaptive generation of cubature points and weight can make the filter reasonably distribute the cubature points and allocate the corresponding weights, which can obviously improve the approximate accuracy of one-step state and measurement prediction. Finally, the superior performance of the proposed filter is demonstrated in a benchmark target tracking model.

Sign in / Sign up

Export Citation Format

Share Document