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.

TARGET TRACKING IN VIDEO SEQUENCES USING WAVELET TRANSFORM

2008 ◽  
Vol 06 (05) ◽  
pp. 675-690 ◽  
Author(s):  
S. ARIVAZHAGAN ◽  
W. SYLVIA LILLY JEBARANI ◽  
G. KUMARAN
Keyword(s):  
Wavelet Transform ◽  
Target Tracking ◽  
Tracking System ◽  
Region Growing ◽  
Video Sequences ◽  
Discrete Wavelet ◽  
Perspective View ◽  
Cluttered Environment ◽  
Detection And Tracking ◽  
Video Frames

Automatic target tracking is a challenging task in video surveillance applications. Here, an offline target-tracking system in video sequences using Discrete Wavelet Transform is presented. The proposed algorithm uses co-occurrence features, derived from sub-bands of discrete wavelet transformed sub-blocks, obtained from individual video frames, to identify a seed in the frame. Then, the region-growing algorithm is applied to detect and track the target. The results of the proposed target detection and tracking system in video sequences are found to be satisfactory. The effectiveness of the target-tracking algorithm has been proved as the target gets detected, irrespective of size of the target, perspective view and cluttered environment.

scholarly journals Dynamic Target Tracking and Ingressing of a Small UAV Using Monocular Sensor Based on the Geometric Constraints

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1931
Author(s):  
Zi-Hao Wang ◽  
Wen-Jie Chen ◽  
Kai-Yu Qin
Keyword(s):  
Root Mean Square Error ◽  
Target Tracking ◽  
Real Time ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Geometric Constraints ◽  
Mean Square ◽  
Moving Targets ◽  
Small Uav ◽  
Dynamic Target

In many applications of airborne visual techniques for unmanned aerial vehicles (UAVs), lightweight sensors and efficient visual positioning and tracking algorithms are essential in a GNSS-denied environment. Meanwhile, many tasks require the ability of recognition, localization, avoiding, or flying pass through these dynamic obstacles. In this paper, for a small UAV equipped with a lightweight monocular sensor, a single-frame parallel-features positioning method (SPPM) is proposed and verified for a real-time dynamic target tracking and ingressing problem. The solution is featured with systematic modeling of the geometric characteristics of moving targets, and the introduction of numeric iteration algorithms to estimate the geometric center of moving targets. The geometric constraint relationships of the target feature points are modeled as non-linear equations for scale estimation. Experiments show that the root mean square error percentage of static target tracking is less than 1.03% and the root mean square error of dynamic target tracking is less than 7.92 cm. Comprehensive indoor flight experiments are conducted to show the real-time convergence of the algorithm, the effectiveness of the solution in locating and tracking a moving target, and the excellent robustness to measurement noises.

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.

The Research of Bionic Robot Dynamic Target Tracking System

2010 ◽  
Vol 33 ◽  
pp. 332-336 ◽  
Author(s):  
P. Wang ◽  
X.F. Ye ◽  
S.C. Kang ◽  
J.L. Xin
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Robot Vision ◽  
Wiener Filtering ◽  
Outer Contour ◽  
Automatic Tracking ◽  
Bionic Robot ◽  
Dynamic Target ◽  
Particle Filter Algorithm

In order to improve the quality of the bionic robot vision tracking, the new automatic tracking algorithm system is proposed in this paper. Based on the completed system hardware design and implementing scheme, the scene noise is removed by adaptive wiener filtering. Through the improved sequential particle filter algorithm, the dynamic target tracking is realized. The experiment result shows that the improved algorithm system still can lock the dynamic target accurately under the condition of that the outer contour of target changing and the partial occlusion existing.

scholarly journals Indoor Detection and Tracking of People Using mmWave Sensor

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xu Huang ◽  
Hasnain Cheena ◽  
Abin Thomas ◽  
Joseph K. P. Tsoi
Keyword(s):  
Tracking System ◽  
Clustering Algorithms ◽  
Computation Time ◽  
Raspberry Pi ◽  
Tracking Accuracy ◽  
Experimental Accuracy ◽  
People Detection ◽  
Position Errors ◽  
Detection And Tracking ◽  
Average Position

This paper proposes a new indoor people detection and tracking system using a millimeter-wave (mmWave) radar sensor. Firstly, a systematic approach for people detection and tracking is presented—a static clutter removal algorithm used for removing mmWave radar data’s static points. Two efficient clustering algorithms are used to cluster and identify people in a scene. The recursive Kalman filter tracking algorithm with data association is used to track multiple people simultaneously. Secondly, a fast indoor people detection and tracking system is designed based on our proposed algorithms. The method is lightweight enough for scalability and portability, and we can execute it in real time on a Raspberry Pi 4. Finally, the proposed method is validated by comparing it with the Texas Instruments (TI) system. The proposed system’s experimental accuracy ranged from 98% (calculated by misclassification errors) for one person to 65% for five people. The average position errors at positions 1, 2, and 3 are 0.2992 meters, 0.3271 meters, and 0.3171 meters, respectively. In comparison, the Texas Instruments system had an experimental accuracy ranging from 96% for one person to 45% for five people. The average position errors at positions 1, 2, and 3 are 0.3283 meters, 0.3116 meters, and 0.3343 meters, respectively. The proposed method’s advantage is demonstrated in terms of tracking accuracy, computation time, and scalability.

Design of the Target Tracking Process Based on DM648

2014 ◽  
Vol 599-601 ◽  
pp. 904-907
Author(s):  
Guang Yu Yao ◽  
Lu Song
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Intelligent Transportation System ◽  
Tracking System ◽  
Low Cost ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Tracking Process ◽  
Wide Range ◽  
Fast Processing

Compared with the traditional vehicle detector, the vehicle detection and tracking based on video image processing and the technique of visual target has fast processing speed, and convenient installation and maintenance, and low cost, wide range of monitoring, can obtain more kinds of traffic parameters, and many other advantages, has become more and more widely used in intelligent transportation system (ITS) in recent years. This paper introduces a method for real-time detection, target tracking in traffic image sequences from a fixed single camera. The System adopts TMS320DM648 as the core processor to implement the real-time target tracking algorithms, mainly complete the effective information real-time display of the software and hardware design of target tracking system, application flexibility, small volume, stable and reliable, it is very practical in practice.

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.

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