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.

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%.

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

scholarly journals SIAMESE NETWORK COMBINED WITH ATTENTION MECHANISM FOR OBJECT TRACKING

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1315-1322
Author(s):  
D. Zhang ◽  
J. Lv ◽  
Z. Cheng ◽  
Y. Bai ◽  
Y. Cao
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Network Model ◽  
Input Image ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Learning Object ◽  
Feature Maps ◽  
Siamese Network ◽  
Network Object

Abstract. After the development of deep learning object tracking methods in recent years, the fully convolutional siamese network object tracking algorithm SiamFC has become a more classic deep learning object tracking algorithm. In view of the problem that the accuracy of the tracking results of SiamFC will be reduced in the case of complex backgrounds, this paper introduces the attention mechanism based on the SiamFC, which performs channel and spatial weighting on the feature maps obtained by convolution of the input image. At the same time, the backbone network model of CNN in the algorithm is adjusted, then the siamese network combined with attention mechanism for object tracking is proposed. It can strengthen the effectiveness of the results of feature extraction and enhance the ability of the network model to discriminate targets. In this paper, the algorithm is tested on the OTB2015, VOT2016 and VOT2017 datasets, and compared with multiple object tracking algorithms. Experimental results show that the algorithm in this paper can better solve the complex background problem in object tracking, and has certain advantages compared with other algorithms.

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.

scholarly journals Remote Sensing and Texture Image Classification Network Based on Deep Learning Integrated with Binary Coding and Sinkhorn Distance

2019 ◽  
Vol 11 (23) ◽  
pp. 2870
Author(s):  
Chu He ◽  
Qingyi Zhang ◽  
Tao Qu ◽  
Dingwen Wang ◽  
Mingsheng Liao
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Feature Fusion ◽  
Texture Features ◽  
Texture Image ◽  
Binary Coding ◽  
Deep Feature ◽  
Feature Based ◽  
Texture Image Classification

In the past two decades, traditional hand-crafted feature based methods and deep feature based methods have successively played the most important role in image classification. In some cases, hand-crafted features still provide better performance than deep features. This paper proposes an innovative network based on deep learning integrated with binary coding and Sinkhorn distance (DBSNet) for remote sensing and texture image classification. The statistical texture features of the image extracted by uniform local binary pattern (ULBP) are introduced as a supplement for deep features extracted by ResNet-50 to enhance the discriminability of features. After the feature fusion, both diversity and redundancy of the features have increased, thus we propose the Sinkhorn loss where an entropy regularization term plays a key role in removing redundant information and training the model quickly and efficiently. Image classification experiments are performed on two texture datasets and five remote sensing datasets. The results show that the statistical texture features of the image extracted by ULBP complement the deep features, and the new Sinkhorn loss performs better than the commonly used softmax loss. The performance of the proposed algorithm DBSNet ranks in the top three on the remote sensing datasets compared with other state-of-the-art algorithms.

A Deep Learning Filter for Visual Drone Single Object Tracking

2020 ◽  
pp. 638-650
Author(s):  
Xin Zhang ◽  
Licheng Jiao ◽  
Xu Liu ◽  
Xiaotong Li ◽  
Wenhua Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Single Object

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

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 73 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document