Siamese single object tracking algorithm with natural language prior

2021 ◽  
Vol 15 (5) ◽  
Author(s):  
Qianli Zhou ◽  
Rong Wang ◽  
Jinze Li ◽  
Naiqian Tian ◽  
Wenjin Zhang
Keyword(s):  
Natural Language ◽  
Object Tracking ◽  
Tracking Algorithm ◽  
Single Object

Design of UAV Single Object Tracking Algorithm Based on Feature Fusion

2021 ◽  
Author(s):  
Changze Li ◽  
Xiaoxiong Liu ◽  
Xingwang Zhang ◽  
Bin Qin
Keyword(s):  
Object Tracking ◽  
Feature Fusion ◽  
Tracking Algorithm ◽  
Single Object

scholarly journals Single object tracking algorithm in occlusion scene based on improved ECO

2021 ◽  
Vol 2132 (1) ◽  
pp. 012010
Author(s):  
Guorong Xie ◽  
Rongqi Jiang ◽  
Yi Qu
Keyword(s):  
Object Tracking ◽  
Success Rate ◽  
Tracking Algorithm ◽  
Single Object ◽  
Feature Map ◽  
Structure Information ◽  
Deep Feature ◽  
Training Samples ◽  
Update Strategy

Abstract To alleviate the occlusion problem in a single object tracking scene, this paper proposes an ECO-MHDU object tracking algorithm with a more powerful anti-occlusion performance based on the ECO tracker. The algorithm first uses the pre-trained MobileNetV3 lightweight backbone network on the ImageNet dataset to replace the ResNet network in the ECO to increase the speed of the algorithm to obtain the shallow and deep feature information of the image, while effectively using the attention mechanism in the MobileNetV3 network to strengthen the algorithm’s ability to extract target features; secondly, use the DropBlock operation on the acquired feature map to generate a random continuous mask on the feature map channel to improve the algorithm’s learning of the global robust spatial structure information; finally, a confidence update strategy is introduced into the GMM sample generation space. To improve the quality of training samples, unreliable tracking states such as confidence detection and occlusion are designed to avoid updating the sample space with damaging information. Compared with the ECO algorithm, the ECO-MHDU algorithm proposed in this paper has a success rate of 68.0% on the occlusion attributes of the OTB100 dataset, which is 2.3% higher than the ECO algorithm, and the ECO-MHDU algorithm also showed the best performance on the entire dataset sequence, with a success rate of 69.3%.

An adaptive template matching-based single object tracking algorithm with parallel acceleration

2019 ◽  
Vol 64 ◽  
pp. 102603 ◽  
Author(s):  
Baicheng Yan ◽  
Limin Xiao ◽  
Hang Zhang ◽  
Daliang Xu ◽  
Li Ruan ◽  
...  
Keyword(s):  
Object Tracking ◽  
Template Matching ◽  
Tracking Algorithm ◽  
Single Object ◽  
Parallel Acceleration

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

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.

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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