Underwater Target Tracking Algorithm Based on an Improved Color Matching

2013 ◽  
Vol 834-836 ◽  
pp. 1234-1239
Author(s):  
Ling Yu Sun ◽  
Ming Ming Li ◽  
Zhao Wang
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Size Variation ◽  
Window Size ◽  
Tracking Algorithm ◽  
Color Matching ◽  
Tracking Accuracy ◽  
Matching Algorithm ◽  
Underwater Image ◽  
Underwater Target

Owing to fuzzy detail and distortion of underwater image and complex changes of target, the underwater target tracking system requires accuracy and continuity of tracking, and expects that the size of tracking window can adapt to appearance change of target. According to the requirements mentioned above, the underwater target tracking algorithm based on an improved color matching is proposed, which finds the best location of target through tracking accuracy algorithm and calculates width of window on the basis of tracking window size variation algorithm. The experimental results show that this algorithm can adaptively track the real-time target and has higher accuracy than traditional color matching algorithm.

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.

scholarly journals Underwater Bearing-only and Bearing-Doppler Target Tracking Based on Square Root Unscented Kalman Filter

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 740 ◽  
Author(s):  
Li ◽  
Zhao ◽  
Yu ◽  
Wei
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Unscented Kalman Filter ◽  
Tracking System ◽  
Positive Definiteness ◽  
Square Root ◽  
Tracking Accuracy ◽  
Underwater Target ◽  
State Observability ◽  
Passive Measurements

Underwater target tracking system can be kept covert using the bearing-only or the bearing-Doppler measurements (passive measurements), which will reduce the risk of been detected. According to the characteristics of underwater target tracking, the square root unscented Kalman filter (SRUKF) algorithm, which is based on the Bayesian theory, was applied to the underwater bearing-only and bearing-Doppler non-maneuverable target tracking problem. Aiming at the shortcomings of the unscented Kalman filter (UKF), the SRUKF uses the QR decomposition and the Cholesky factor updating, in order to avoid that the process noise covariance matrix loses its positive definiteness during the target tracking period. The SRUKF uses sigma sampling to avoid the linearization of the nonlinear bearing-only and the bearing-Doppler measurements. To ensure the target state observability in underwater target tracking, the paper uses single maneuvering observer to track the single non-maneuverable target. The simulation results show that the SRUKF has better tracking performance than the extended Kalman filter (EKF) and the UKF in tracking accuracy and stability, and the computational complexity of the SRUKF algorithm is low.

scholarly journals Research on Target Tracking Algorithm Based on Siamese Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haibo Pang ◽  
Qi Xuan ◽  
Meiqin Xie ◽  
Chengming Liu ◽  
Zhanbo Li
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Evaluation Criteria ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Temporal Attention ◽  
Tracking Accuracy ◽  
Current Frame ◽  
Tracking Process ◽  
Recognition Ability

Target tracking is a significant topic in the field of computer vision. In this paper, the target tracking algorithm based on deep Siamese network is studied. Aiming at the situation that the tracking process is not robust, such as drift or miss the target, the tracking accuracy and robustness of the algorithm are improved by improving the feature extraction part and online update part. This paper adds SE-block and temporal attention mechanism (TAM) to the framework of Siamese neural network. SE-block can refine and extract features; different channels are given different weights according to their importance which can improve the discrimination of the network and the recognition ability of the tracker. Temporal attention mechanism can update the target state by adjusting the weights of samples at current frame and historical frame to solve the model drift caused by the existence of similar background. We use cross-entropy loss to distinguish the targets in different sequences so that their distance in the feature domains is longer and the features are easier to identify. We train and test the network on three benchmarks and compare with several state-of-the-art tracking methods. The experimental results demonstrate that the algorithm proposed is superior to other methods in tracking effect diagram and evaluation criteria. The proposed algorithm can solve the occlusion problem effectively while ensuring the real-time performance in the process of tracking.

Tracking algorithm of snowboard target in intelligent system

2020 ◽  
pp. 1-9
Author(s):  
Zhipeng Li ◽  
Xiaolan Li ◽  
Ming Shi ◽  
Wenli Song ◽  
Guowei Zhao ◽  
...  
Keyword(s):  
Target Tracking ◽  
High Speed ◽  
Intelligent System ◽  
Mean Shift ◽  
Target Position ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Mean Shift Algorithm ◽  
Target Track ◽  
The Mean

Snowboarding is a kind of sport that takes snowboarding as a tool, swivels and glides rapidly on the specified slope line, and completes all kinds of difficult actions in the air. Because the sport is in the state of high-speed movement, it is difficult to direct guidance during the sport, which is not conducive to athletes to find problems and correct them, so it is necessary to track the target track of snowboarding. The target tracking algorithm is the main solution to this task, but there are many problems in the existing target tracking algorithm that have not been solved, especially the target tracking accuracy in complex scenes is insufficient. Therefore, based on the advantages of the mean shift algorithm and Kalman algorithm, this paper proposes a better tracking algorithm for snowboard moving targets. In the method designed in this paper, in order to solve the problem, a multi-algorithm fusion target tracking algorithm is proposed. Firstly, the SIFT feature algorithm is used for rough matching to determine the fuzzy position of the target. Then, the good performance of the mean shift algorithm is used to further match the target position and determine the exact position of the target. Finally, the Kalman filtering algorithm is used to further improve the target tracking algorithm to solve the template trajectory prediction under occlusion and achieve the target trajectory tracking algorithm design of snowboarding.

Linear Multi-Target Integrated Probabilistic Data Association Filter With Automatic Track Management for Autonomous Vehicles

2018 ◽  
Author(s):  
Andinet Hunde ◽  
Beshah Ayalew
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Data Association ◽  
Tracking Algorithm ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Address Data ◽  
Multi Target Tracking ◽  
Track Management ◽  
Track Initiation

Target tracking in public traffic calls for a tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. In addition, the key problem of data association needs to be handled effectively considering the limitations in the computational resources onboard an autonomous car. In this paper, we discuss a multi-target tracking system that addresses target birth/initiation and death/termination processes with automatic track management feature. The tracking system is based on Linear Multi-target Integrated Probabilistic Data Association Filter (LMIPDAF), which is adapted to specifically include algorithms that handle track initiation and termination, clutter density estimation and track management. The performance of the proposed tracking algorithm is compared to other single and multi-target tracking schemes and is shown to have acceptable tracking error. It is further illustrated through multiple traffic simulations that the computational requirement of the tracking algorithm is less than that of optimal multi-target tracking algorithms that explicitly address data association uncertainties.

scholarly journals Research on the Particle Filter Single-Station Target Tracking Algorithm Based on Particle Number Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lieping Zhang ◽  
Jinghua Nie ◽  
Shenglan Zhang ◽  
Yanlin Yu ◽  
Yong Liang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Particle Number ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Time Performance ◽  
Single Station ◽  
Tracking Model ◽  
Number Of Particles

Given that the tracking accuracy and real-time performance of the particle filter (PF) target tracking algorithm are greatly affected by the number of sampled particles, a PF target tracking algorithm based on particle number optimization under the single-station environment was proposed in this study. First, a single-station target tracking model was established, and the corresponding PF algorithm was designed. Next, a tracking simulation experiment was carried out on the PF target tracking algorithm under different numbers of particles with the root mean square error (RMSE) and filtering time as the evaluation indexes. On this basis, the optimal number of particles, which could meet the accuracy and real-time performance requirements, was determined and taken as the number of particles of the proposed algorithm. The MATLAB simulation results revealed that compared with the unscented Kalman filter (UKF), the single-station PF target tracking algorithm based on particle number optimization not only was of high tracking accuracy but also could meet the real-time performance requirement.

A Novel Improved Multi-Point Matching Based Coronary Disease Quantitative Analysis for Speckle Tracking in Ultrasound Image

2020 ◽  
Vol 10 (2) ◽  
pp. 489-495 ◽  
Author(s):  
Liu Hui ◽  
Ye Xinhua
Keyword(s):  
Coronary Disease ◽  
Speckle Tracking ◽  
Tracking Error ◽  
Block Matching ◽  
Tracking Algorithm ◽  
Ultrasound Images ◽  
Tracking Accuracy ◽  
Matching Algorithm ◽  
Block Matching Algorithm ◽  
Full Search

Ultrasonic imaging technology is one of the most direct and effective methods, which can prevent, treat cardiovascular disease, and it can make noninvasive observation and detection of cardiac motion. To calculate accurate myocardial motion parameters more directly, effectively and accurately, speckle tracking algorithm is put forward. However, the performance of speckle tracking is directly related to the diagnosis effect of coronary disease. Compared with the speckle tracking algorithm based on optical flow algorithm, the speckle tracking algorithm based on block matching algorithm has advantages of higher tracking accuracy and faster computation speed and better anti-interference immunity. According to the requirements of clinical application, the full search block matching algorithm whose accuracy is the highest is adopted as the basic algorithm of the speckle tracking algorithm. In paper, In view of the shortcomings of the full search block matching algorithm, the algorithm based on calculating tracking error is proposed. The speckle tracking algorithm based on calculating tracking error has good tracking results, which shows that this algorithm can effectively improve the applicability, stability and accuracy of tracking in different ultrasound images and multi-point tracking in the same ultrasound images.

Efficient Moving Target Tracking Algorithm: Design and Implementation of the Sand-Table Tracking Algorithm

2013 ◽  
Vol 475-476 ◽  
pp. 1032-1039
Author(s):  
Jia Qi Li
Keyword(s):  
Target Tracking ◽  
Algorithm Design ◽  
Tracking Algorithm ◽  
Moving Target ◽  
Tracking Accuracy ◽  
Moving Target Tracking ◽  
Design And Implementation ◽  
Tracking Ability

Working on the design of a new algorithm :sand_table algorithm.The algorithm could work well in recognizing and tracking an single moving target shot by camera or in a video .The algorithm works simple with low operation cost.May used in tracking different object of many kinds.The algorithm imitate the the process of falling sands to Greatly enhance the tracking ability and tracking accuracy.

Research of Dynamic Target Tracking Algorithm

2013 ◽  
Vol 448-453 ◽  
pp. 3601-3604
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Jie Li
Keyword(s):  
Target Tracking ◽  
Cross Correlation ◽  
Search Algorithm ◽  
Automatic Recognition ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Matching Algorithm ◽  
Normalized Cross Correlation ◽  
Dynamic Target ◽  
Correlation Matching

An improved correlation matching algorithm is proposed in order to overcome some shortcomings of detecting the position of object accurately. A tracking algorithm with normalized cross correlation is introduced. In order to enhance the match speed, we have adopted pyramid search algorithm. The experimental results show that the algorithm has characteristics including automatic recognition of the object; permitting tracking and prediction when the object become shaded; the algorithm makes adaptive decision of varied object during the process of tracking.

The Design of Embedded Detecting and Tracking System

2011 ◽  
Vol 204-210 ◽  
pp. 1960-1963
Author(s):  
Zhong Hai Li ◽  
Dan Liu ◽  
Jian Guo Cui ◽  
Shen Li
Keyword(s):  
Signal Processing ◽  
Embedded System ◽  
Target Tracking ◽  
Real Time ◽  
Computer Technology ◽  
Tracking System ◽  
Tracking Algorithm ◽  
Embedded Technology ◽  
Target Detecting

On the basis of analyzing the character of target detecting and tracking algorithm, referencing the successful application of embedded system in the fields of electronics，signal processing and computer technology, combining target detecting and tracking and embedded technology, an embedded target tracking system is proposed which based on s3c24lO on which running clipping Linux system, and a tracking example of flying target is given. The whole system reaches the target of small size and good real-time. It’s a useful attempt to realize the small and intelligent of target tracking system.

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