Detection and Tracking Recognition Algorithm for Weak Moving Target

2014 ◽  
Vol 513-517 ◽  
pp. 3199-3202 ◽  
Author(s):  
Xiang Jie Niu ◽  
Bin Lan
Keyword(s):  
Feature Selection ◽  
Target Tracking ◽  
Moving Objects ◽  
Recognition Algorithm ◽  
Moving Target ◽  
Image Process ◽  
Moving Targets ◽  
Image Detection ◽  
Image Preprocessing ◽  
Detection And Tracking

According to features of detection, tracking and identification technology of weak moving targets in image process, the paper simply introduces its technical difficulties and mainly discusses the detection and tracking algorithm for the weak moving targets. In accordance with three steps including the image preprocessing, feature selection and target tracking, the paper designs images' detection and tracking recognition algorithm for the weak moving objects in strong noise background, and describes the specific execution methods and procedures, which has a certain significance to further improve image detection for the weak moving targets.

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.

An Industrial Production Line Dynamic Target Tracking System Based on HAAR and CAMSHIFT

2020 ◽  
Vol 34 (11) ◽  
pp. 2059037
Author(s):  
Xuejun Tian ◽  
Haowen Feng ◽  
Jieyan Chen
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Raspberry Pi ◽  
Automation System ◽  
Moving Targets ◽  
Embedded Platform ◽  
System A ◽  
Detection And Tracking ◽  
Camshift Algorithm ◽  
Dynamic Target

Aiming at the detection and tracking of moving targets in industrial automation system, a dynamic target tracking algorithm based on HAAR and CAMSHIFT is proposed. A cascade HAAR classifier is designed and trained for tracking targets. CAMSHIFT algorithm is used to track and detect moving targets quickly. The system is tested on Raspberry Pi embedded platform. The results show that the algorithm can detect the target correctly and track the target effectively.

A General Identification Process of Moving Cells Based on MATLAB

2013 ◽  
Vol 427-429 ◽  
pp. 1981-1986
Author(s):  
Zhi Peng Chen ◽  
Tian Liang ◽  
Jian Wang ◽  
Wen Chao Qin ◽  
Tian Guan
Keyword(s):  
Target Recognition ◽  
Processing Algorithm ◽  
Target Cells ◽  
Moving Target ◽  
Matlab Simulation ◽  
Moving Targets ◽  
Image Preprocessing ◽  
Identification Process ◽  
Simulation Tools ◽  
Processing Steps

This paper proposed and implemented a general identification process of videos containing moving target cells, using mature digital image processing technology and MATLAB simulation tools. The processing steps included video sub-framing, image reading, image preprocessing, target recognition, and etc. For videos containing moving target cells, three groups of experiments were tested to verify the feasibility of the processing algorithm. Results found that the algorithm could accurately identify the targets in the videos. Thus, the presented processing algorithm is acceptable to be a general identification method for videos containing moving targets.

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.

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.

Comparative Study of Moving Target Analysis System

2014 ◽  
Vol 989-994 ◽  
pp. 3122-3126
Author(s):  
Min Feng ◽  
Huai Chang Du
Keyword(s):  
Object Tracking ◽  
Tracking System ◽  
Moving Target ◽  
Motion Trajectory ◽  
Moving Targets ◽  
Image System ◽  
Target Analysis ◽  
Detection And Tracking ◽  
Analysis System ◽  
Experiment Analysis

This paper compares two kinds of moving target analysis systems, which are the motion history image system and the moving object tracking system. Each system includes two parts which are moving target detection and tracking, achieving respectively detection of the direction of moving targets or representation of motion trajectory. Through experiment analysis of moving human and vehicles, each system is determined which situation it is suitable for.

scholarly journals Extraction and Detection of Surface Defects in Particleboards by Tracking Moving Targets

Algorithms ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 6 ◽  
Author(s):  
Chengcheng Wang ◽  
Yaqiu Liu ◽  
Peiyu Wang
Keyword(s):  
Target Tracking ◽  
Surface Defect ◽  
Surface Defects ◽  
Moving Target ◽  
Moving Targets ◽  
Sobel Operator ◽  
Defect Area ◽  
Flow Algorithm ◽  
Fuzzy Pattern ◽  
Kernel Correlation

Considering the linear motion of particleboards in the production line, the detection of surface defects in particleboards is a major challenge. In this paper, a method based on moving target tracking is proposed for the detection of surface defects in particleboards. To achieve this, the kernel correlation filter (KCF) target tracking algorithm was modified with the median flow algorithm and used to capture the moving targets of surface defects. The defect images were extracted by a Sobel operator, and the defect number, the defect area, and the degree of damage were calculated. The level of surface defect in particleboards was evaluated by fuzzy pattern recognition. Experiments were then carried out to prove the effectiveness and accuracy of the proposed method.

scholarly journals Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR

2020 ◽  
Vol 12 (18) ◽  
pp. 3083 ◽  
Author(s):  
Xiaqing Yang ◽  
Jun Shi ◽  
Yuanyuan Zhou ◽  
Chen Wang ◽  
Yao Hu ◽  
...  
Keyword(s):  
Target Tracking ◽  
Bp Algorithm ◽  
Synthetic Aperture ◽  
Moving Target ◽  
Back Projection ◽  
Moving Targets ◽  
New Approach ◽  
Moving Target Tracking ◽  
Accuracy Requirement ◽  
Multiple Moving Targets

Stable and efficient ground moving target tracking and refocusing is a hard task in synthetic aperture radar (SAR) data processing. Since shadows in video-SAR indicate the actual positions of moving targets at different moments without any displacement, shadow-based methods provide a new approach for ground moving target processing. This paper constructs a novel framework to refocus ground moving targets by using shadows in video-SAR. To this end, an automatic-registered SAR video is first obtained using the video-SAR back-projection (v-BP) algorithm. The shadows of multiple moving targets are then tracked using a learning-based tracker, and the moving targets are ultimately refocused via a proposed moving target back-projection (m-BP) algorithm. With this framework, we can perform detecting, tracking, imaging for multiple moving targets integratedly, which significantly improves the ability of moving-target surveillance for SAR systems. Furthermore, a detailed explanation of the shadow of a moving target is presented herein. We find that the shadow of ground moving targets is affected by a target’s size, radar pitch angle, carrier frequency, synthetic aperture time, etc. With an elaborate system design, we can obtain a clear shadow of moving targets even in X or C band. By numerical experiments, we find that a deep network, such as SiamFc, can easily track shadows and precisely estimate the trajectories that meet the accuracy requirement of the trajectories for m-BP.

Moving Target Detection and Tracking Based on Improved FCM Algorithm

2020 ◽  
Vol 14 (1) ◽  
pp. 63-74 ◽  
Author(s):  
Wang Ke Feng ◽  
Sheng Xiao Chun
Keyword(s):  
Target Detection ◽  
Clustering Algorithm ◽  
Rapid Development ◽  
Moving Target ◽  
Moving Targets ◽  
Moving Target Detection ◽  
Complex Situation ◽  
Fcm Algorithm ◽  
Detection And Tracking ◽  
Moving Vehicles

With the rapid development of computer intelligence technology, the majority of scholars have a great interest in the detection and tracking of moving targets in the field of video surveillance and have been involved in its research. Moving target detection and tracking has also been widely used in military, industrial control, and intelligent transportation. With the rapid progress of the social economy, the supervision of traffic has become more and more complicated. How to detect the vehicles on the road in real time, monitor the illegal vehicles, and control the illegal vehicles effectively has become a hot issue. In view of the complex situation of moving vehicles in various traffic videos, the authors propose an improved algorithm for effective detection and tracking of moving vehicles, namely improved FCM algorithm. It combines traditional FCM algorithm with genetic algorithm and Kalman filter algorithm to track and detect moving targets. Experiments show that this improved clustering algorithm has certain advantages over other clustering algorithms.

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