Object Tracking Using Improved Meanshift Algorithm Combined with Kalman Filter on Independent Visual Robotic Fish

2013 ◽  
Vol 333-335 ◽  
pp. 1030-1033
Author(s):  
Hai Hua Shi ◽  
Wei Xiang
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Target Tracking ◽  
Renewal Process ◽  
Robotic Fish ◽  
Moving Target ◽  
Complex Environment ◽  
Filter Model ◽  
Single Target ◽  
Single Target Tracking

This Paper Investigates an Improved Meanshift Algorithm Combined with Kalman Filter Aiming at Failure of a Target Tracking in Complex Environment for the Independent Visual Robotic Fish. First,we Need to Establish Kalman Filter Model of Moving Target. then,the Prediction and Renewal Process of Kalman Filter are Applied into the Meanshift Tracking Algorithm. Experimental Results Show that Improved Algorithm can Effectively Improve the Performance of Single Target Tracking in Complex Environment, and Realize Continuous Tracking of a Target. also, it can Obtain more Reliable Tracking Effect, and can be Used for more Complicated Scenes.

The Target Tracking Algorithm Research of Independent Vision Robot Fish

2013 ◽  
Vol 753-755 ◽  
pp. 2015-2019
Author(s):  
Ming Zhang ◽  
Li Wang ◽  
Hai Hua Shi ◽  
Wei Xiang
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Water Wave ◽  
Tracking Algorithm ◽  
Complex Environment ◽  
Single Target ◽  
Robot Fish ◽  
Single Target Tracking ◽  
Combined Algorithm

In the independent vision robot fish games, the interference of water wave often causes tracking inaccuracy and target tracking failure. In order to solve these problems, the Meanshift algorithm and the combination of Meanshift algorithm and Kalman filter respectively are studied to realize target tracking of independent vision robot fish in this paper. By comparing the two algorithms, the results show that: the former tracking algorithm is not ideal and easy to lose the target. The combined algorithm of Meanshift and Kalman filter can effectively improve the performance of single-target tracking in a complex environment to achieve the goal of continuous accurate tracking.

Multitarget Tracking Using Siamese Neural Networks

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-16
Author(s):  
Na An ◽  
Wei Qi Yan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Detection ◽  
Object Tracking ◽  
Target Tracking ◽  
Deep Neural Networks ◽  
Multitarget Tracking ◽  
Siamese Network ◽  
Single Target ◽  
Single Target Tracking

In this article, we detect and track visual objects by using Siamese network or twin neural network. The Siamese network is constructed to classify moving objects based on the associations of object detection network and object tracking network, which are thought of as the two branches of the twin neural network. The proposed tracking method was designed for single-target tracking, which implements multitarget tracking by using deep neural networks and object detection. The contributions of this article are stated as follows. First, we implement the proposed method for visual object tracking based on multiclass classification using deep neural networks. Then, we attain multitarget tracking by combining the object detection network and the single-target tracking network. Next, we uplift the tracking performance by fusing the outcomes of the object detection network and object tracking network. Finally, we speculate on the object occlusion problem based on IoU and similarity score, which effectively diminish the influence of this issue in multitarget tracking.

scholarly journals Target Tracking Using Kalman Filter Based Algorithms

2021 ◽  
Vol 2078 (1) ◽  
pp. 012020
Author(s):  
Jiankun Ling
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Information Gathering ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Multiple Targets ◽  
Multiple Hypothesis ◽  
Multiple Hypothesis Tracking ◽  
Single Target ◽  
Single Target Tracking

Abstract Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.

Study on the Characteristics of the Automatic Tracking of Multiple Target Tracking

2014 ◽  
Vol 496-500 ◽  
pp. 1564-1567
Author(s):  
Jing Feng He ◽  
Ming Ji ◽  
Song Cheng ◽  
Ya Nan Wang
Keyword(s):  
Target Tracking ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Multiple Targets ◽  
Automatic Tracking ◽  
Single Target ◽  
Development Direction ◽  
The Future ◽  
Single Target Tracking

Based on introducing the traditional scan and single target tracking state, focuses on the automatic tracking characteristics of each stage under the condition of multiple targets. The two form of automatic tracking multiple targets, and the development direction of the future.

Radar selection for single-target tracking in radar networks

2015 ◽  
Author(s):  
Xueting Li ◽  
Wei Yi ◽  
Guolong Cui ◽  
Lingjiang Kong ◽  
Xiaobo Yang
Keyword(s):  
Target Tracking ◽  
Radar Networks ◽  
Single Target ◽  
Selection For ◽  
Single Target Tracking
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