Object Tracking Algorithm Based on Margin Feature Fusion

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.

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An Object Tracking Algorithm Based on the "Current" Statistical Model and the Multi-Feature Fusion

Journal of Software ◽

10.4304/jsw.7.9.2000-2008 ◽

2012 ◽

Vol 7 (9) ◽

Cited By ~ 4

Author(s):

Jinhua Wang ◽

Jie Cao ◽

Di Wu ◽

Yabing Yu

Keyword(s):

Statistical Model ◽

Object Tracking ◽

Feature Fusion ◽

Tracking Algorithm

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Object Tracking Algorithm Based on Dual Color Feature Fusion with Dimension Reduction

Sensors ◽

10.3390/s19010073 ◽

2018 ◽

Vol 19 (1) ◽

pp. 73 ◽

Cited By ~ 2

Author(s):

Shuo Hu ◽

Yanan Ge ◽

Jianglong Han ◽

Xuguang Zhang

Keyword(s):

Dimension Reduction ◽

Object Tracking ◽

Feature Fusion ◽

Target Position ◽

Input Image ◽

Tracking Algorithm ◽

Tracking Accuracy ◽

Color Feature ◽

Color Distribution ◽

Dual Color

Aiming at the problem of poor robustness and the low effectiveness of target tracking in complex scenes by using single color features, an object-tracking algorithm based on dual color feature fusion via dimension reduction is proposed, according to the Correlation Filter (CF)-based tracking framework. First, Color Name (CN) feature and Color Histogram (CH) feature extraction are respectively performed on the input image, and then the template and the candidate region are correlated by the CF-based methods, and the CH response and CN response of the target region are obtained, respectively. A self-adaptive feature fusion strategy is proposed to linearly fuse the CH response and the CN response to obtain a dual color feature response with global color distribution information and main color information. Finally, the position of the target is estimated, based on the fused response map, with the maximum of the fused response map corresponding to the estimated target position. The proposed method is based on fusion in the framework of the Staple algorithm, and dimension reduction by Principal Component Analysis (PCA) on the scale; the complexity of the algorithm is reduced, and the tracking performance is further improved. Experimental results on quantitative and qualitative evaluations on challenging benchmark sequences show that the proposed algorithm has better tracking accuracy and robustness than other state-of-the-art tracking algorithms in complex scenarios.

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Object tracking algorithm based on the feature fusion points

JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT ◽

10.3724/sp.j.1187.2010.00536 ◽

2010 ◽

Vol 24 (6) ◽

pp. 536-541 ◽

Cited By ~ 1

Author(s):

Jinhua Wang ◽

Jie Cao ◽

Yu Li ◽

Chongyu Ren

Keyword(s):

Object Tracking ◽

Feature Fusion ◽

Tracking Algorithm

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A Feature Fusion Based Object Tracking Algorithm

Communications in Computer and Information Science - Cognitive Systems and Signal Processing ◽

10.1007/978-981-16-2336-3_52 ◽

2021 ◽

pp. 548-557

Author(s):

Junwei Hu ◽

Jinlong Chen ◽

Minghao Yang ◽

Yiming Jiang

Keyword(s):

Object Tracking ◽

Feature Fusion ◽

Tracking Algorithm

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Single-Object Tracking Algorithm Based on Two-Step Spatiotemporal Deep Feature Fusion in a Complex Surveillance Scenario

Mathematical Problems in Engineering ◽

10.1155/2021/6653954 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yanyan Chen ◽

Rui Sheng

Keyword(s):

Deep Learning ◽

Object Tracking ◽

Feature Fusion ◽

Engineering Application ◽

Tracking Algorithm ◽

Single Object ◽

Spatiotemporal Information ◽

Video Frames ◽

Deep Feature ◽

Active Research

Object tracking has been one of the most active research directions in the field of computer vision. In this paper, an effective single-object tracking algorithm based on two-step spatiotemporal feature fusion is proposed, which combines deep learning detection with the kernelized correlation filtering (KCF) tracking algorithm. Deep learning detection is adopted to obtain more accurate spatial position and scale information and reduce the cumulative error. In addition, the improved KCF algorithm is adopted to track and calculate the temporal information correlation of gradient features between video frames, so as to reduce the probability of missing detection and ensure the running speed. In the process of tracking, the spatiotemporal information is fused through feature analysis. A large number of experiment results show that our proposed algorithm has more tracking performance than the traditional KCF algorithm and can efficiently continuously detect and track objects in different complex scenes, which is suitable for engineering application.

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