Object tracking algorithm based on feature matching under complex scenes

2015 ◽  
pp. 467-472
Keyword(s):  
Object Tracking ◽  
Feature Matching ◽  
Tracking Algorithm ◽  
Complex Scenes

scholarly journals Robust Object Tracking Algorithm for Autonomous Vehicles in Complex Scenes

2021 ◽  
Vol 13 (16) ◽  
pp. 3234
Author(s):  
Jingwei Cao ◽  
Chuanxue Song ◽  
Shixin Song ◽  
Feng Xiao ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Object Tracking ◽  
Autonomous Vehicles ◽  
Feature Fusion ◽  
Environmental Perception ◽  
Tracking Algorithm ◽  
Tracking Performance ◽  
Tracking Accuracy ◽  
Complex Scenes ◽  
Driving Assistance ◽  
Public Datasets

Object tracking is an essential aspect of environmental perception technology for autonomous vehicles. The existing object tracking algorithms can only be applied well to simple scenes. When the scenes become complex, the algorithms have poor tracking performance and insufficient robustness, and the problems of tracking drift and object loss are prone to occur. Therefore, a robust object tracking algorithm for autonomous vehicles in complex scenes is proposed. Firstly, we study the Siam-FC network and related algorithms, and analyze the problems that need to be addressed in object tracking. Secondly, the construction of a double-template Siamese network model based on multi-feature fusion is described, as is the use of the improved MobileNet V2 as the feature extraction backbone network, and the attention mechanism and template online update mechanism are introduced. Finally, relevant experiments were carried out based on public datasets and actual driving videos, with the aim of fully testing the tracking performance of the proposed algorithm on different objects in a variety of complex scenes. The results showed that, compared with other algorithms, the proposed algorithm had high tracking accuracy and speed, demonstrated stronger robustness and anti-interference abilities, and could still accurately track the object in real time without the introduction of complex structures. This algorithm can be effectively applied in intelligent vehicle driving assistance, and it will help to promote the further development and improvement of computer vision technology in the field of environmental perception.

Object Tracking Algorithm Based on Global Feature Matching Processing of Laser Point Cloud

2020 ◽  
Vol 57 (6) ◽  
pp. 061012
Author(s):  
钱其姝 Qian Qishu ◽  
胡以华 Hu Yihua ◽  
赵楠翔 Zhao Nanxiang ◽  
李敏乐 Li Minle ◽  
邵福才 Shao Fucai
Keyword(s):  
Object Tracking ◽  
Point Cloud ◽  
Feature Matching ◽  
Tracking Algorithm ◽  
Global Feature

Research of Object Tracking Algorithm Based on BRISK

2014 ◽  
Vol 1049-1050 ◽  
pp. 1496-1501
Author(s):  
Shi Xu Guo ◽  
Jia Xin Chen ◽  
Bo Peng
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Feature Matching ◽  
Tracking Algorithm ◽  
Experimental Result ◽  
Moving Target ◽  
Target Template ◽  
Result Show ◽  
Search Feature ◽  
Set Up

In view of the problems that high complexity, large calculation and the difficulty to apply to real-time systems in the current moving target tracking algorithm, this paper introduce the BRISK feature extraction algorithm, and proposed the object tracking algorithm based on BRISK. Set up the background model and use the background difference method to detect the moving target template. Then match in the next frame and track the target. In order to reduce the search feature matching area, further improve the real-time of the algorithm, we also introduce the kalman filter algorithm to estimate the target motion trajectory. The experimental result show that comparing with the SURF, SIFT feature tracking algorithm, the algorithm of this paper has greatly improved in real-time.

High speed long-term visual object tracking algorithm for real robot systems

2021 ◽  
Vol 434 ◽  
pp. 268-284
Author(s):  
Muxi Jiang ◽  
Rui Li ◽  
Qisheng Liu ◽  
Yingjing Shi ◽  
Esteban Tlelo-Cuautle
Keyword(s):  
Object Tracking ◽  
High Speed ◽  
Tracking Algorithm ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Robot Systems ◽  
Real Robot
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