Vision tracking based on adaptive interactive fusion

2020 ◽  
Vol 39 (6) ◽  
pp. 9037-9044
Author(s):  
Junyan Shi ◽  
Han Jiang
Keyword(s):  
Target Tracking ◽  
Personnel Management ◽  
Template Matching ◽  
Feature Fusion ◽  
Research Work ◽  
Feature Representation ◽  
Moving Target ◽  
Moving Targets ◽  
Tracking Method ◽  
Complex Scenes

Under the influence of COVID-19, detection and identification of moving targets are very important for personnel management. A lot of research work has improved the accuracy and robustness of the moving target tracking method, but the recognition accuracy of the traditional target tracking method in complex scenes (lighting changes, background interference, posture changes and other factors) is not satisfactory. In this paper, in view of the limitations of single feature representation of target objects, the method of fusion of HSV color features and edge direction features is used to identify and detect moving targets. In each frame of the tracking process, the weight of each feature is adjusted adaptively according to the proposed fusion strategy, and the position of the target is located by using the method of double template matching. Experiments show that the proposed tracking algorithm based on multi feature fusion can meet the requirements of moving target recognition in complex scenes. The method proposed in this paper has a certain reference value for personnel management under the influence of COVID-19.

An Improved Trace Method for Moving Target under Occlusion

2014 ◽  
Vol 672-674 ◽  
pp. 1931-1934
Author(s):  
Yu Bing Dong ◽  
Guang Liang Cheng ◽  
Ming Jing Li
Keyword(s):  
Mathematical Model ◽  
Target Tracking ◽  
Difficult Problem ◽  
Block Matching ◽  
Experimental Results ◽  
Polar Coordinates ◽  
Moving Target ◽  
Trajectory Prediction ◽  
Tracking Method

Occlusion is a difficult problem to be solved in the process of target tracking. In order to solve the problem of occlusion, a new tracking method combined with trajectory prediction and multi-block matching is presented and studied,and a mathematical model of trajectory prediction of moving target is established in polar coordinates and verified through some experiments. The experimental results show that the new tracking method can be better to trace and forecast the moving target under occlusion.

Research on Underwater Target Tracking Based on Contour Detection

2011 ◽  
Vol 317-319 ◽  
pp. 890-896
Author(s):  
Ming Jun Zhang ◽  
Yuan Yuan Wan ◽  
Zhen Zhong Chu
Keyword(s):  
Prediction Model ◽  
Target Tracking ◽  
Tracking System ◽  
Contour Detection ◽  
Motion Prediction ◽  
Moving Target ◽  
Contour Extraction ◽  
Tracking Accuracy ◽  
Tracking Method ◽  
Underwater Target

The traditional centroid tracking method over-relies on the accuracy of segment, which easily lead to loss of underwater moving target. This paper presents an object tracking method based on circular contour extraction, combining region feature and contour feature. Through the correction to circle features, the problem of multiple solutions causing by Hough transform circle detection is avoided. A new motion prediction model is constructed to make up the deficiency that three-order motion prediction model has disadvantage of high dimension and large calculation. The predicted position of object centroid is updated and corrected by circle contour, forming prediction-measurement-updating closed-loop target tracking system. To reduce system processing time, on the premise of the tracking accuracy, a dynamic detection method based on target state prediction model is proposed. The results of contour extraction and underwater moving target experiments demonstrate the effectiveness of the proposed method.

Feature Fusion Video Target Tracking Method Based on Convolutional Neural Network

2020 ◽  
Vol 57 (4) ◽  
pp. 041502
Author(s):  
刘美菊 Liu Meiju ◽  
曹永战 Cao Yongzhan ◽  
朱树云 Zhu Shuyun ◽  
杨尚奎 Yang Shangkui
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Tracking ◽  
Feature Fusion ◽  
Tracking Method ◽  
Video Target Tracking

scholarly journals Multi-Target Tracking Using Windowed Fourier Single-Pixel Imaging

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7934
Author(s):  
Jinyu Zhang ◽  
Taiyang Hu ◽  
Xiaolang Shao ◽  
Mengxuan Xiao ◽  
Yingjiao Rong ◽  
...  
Keyword(s):  
Target Tracking ◽  
Joint Estimation ◽  
Frame Rate ◽  
Moving Targets ◽  
Tracking Method ◽  
Close Proximity ◽  
Multi Target Tracking ◽  
Video Experiment ◽  
First Time ◽  
Single Pixel

The single-pixel imaging (SPI) technique enables the tracking of moving targets at a high frame rate. However, when extended to the problem of multi-target tracking, there is no effective solution using SPI yet. Thus, a multi-target tracking method using windowed Fourier single-pixel imaging (WFSI) is proposed in this paper. The WFSI technique uses a series of windowed Fourier basis patterns to illuminate the target. This method can estimate the displacements of K independently moving targets by implementing 6K measurements and calculating 2K windowed Fourier coefficients, which is a measurement method with low redundancy. To enhance the capability of the proposed method, we propose a joint estimation approach for multi-target displacement, which solves the problem where different targets in close proximity cannot be distinguished. Using the independent and joint estimation approaches, multi-target tracking can be implemented with WFSI. The accuracy of the proposed multi-target tracking method is verified by numerical simulation to be less than 2 pixels. The tracking effectiveness is analyzed by a video experiment. This method provides, for the first time, an effective idea of multi-target tracking using SPI.

The Application of IMM-PF in Airport Surface Moving Target Tracking

2014 ◽  
Vol 1056 ◽  
pp. 240-243
Author(s):  
Qian Chen ◽  
Bang Feng Wang ◽  
Shu Lin Liu
Keyword(s):  
Target Tracking ◽  
Moving Target ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Moving Targets ◽  
Constant Acceleration ◽  
Moving Target Tracking ◽  
Motion Models ◽  
Ca Model ◽  
Cv Model

In order to improve the accuracy of surveillance for the airport surface moving targets, the interacting multiple model (IMM) algorithm, adopting three motion models including the constant velocity (CV) model, the constant acceleration (CA) model and the constant turning (CT) model, is combined with the particle filter (PF) algorithm. Besides, the airport map information is utilized to amend the measured data and the output estimates so as to further improve the accuracy of airport surface moving target tracking. Numerical simulations show that the improved algorithm described in this paper is more feasible and effective in tracking the airport surface moving targets.

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.

Research on the Mean Shift Target Tracking and Recognition Technology Based on Multi-Feature Fusion

2013 ◽  
Vol 705 ◽  
pp. 561-564 ◽  
Author(s):  
Yong Jun Peng ◽  
Fen Tan ◽  
Jun Sun
Keyword(s):  
Target Tracking ◽  
Video Sequence ◽  
Feature Fusion ◽  
Mean Shift ◽  
Moving Target ◽  
Compressed Domain ◽  
Target Movement ◽  
Moving Target Tracking ◽  
Template Update ◽  
The Mean

This article topics based on multi-feature fusion the Mean shift target tracking technology belongs to the field of intelligent video analysis, moving target tracking is interested in moving target location each image in a video sequence to find and acquire the target movement. Moving target tracking problem can be stated as interested in moving target movement prediction in the video sequence, feature extraction, feature matching and template update problem. In this paper, we consider using compressed domain features as a complement of the color features to extract the compressed domain features first need to understand the compressed domain detection technology. Detection based on the compressed domain, that is, in the case of not decoding or a small amount of decoding, directly on the compression characteristics of the image analysis, in order to achieve the detection of the image moving object.

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