scholarly journals Research on Target Tracking Algorithm of Twin Networks Integrating Attention Mechanism

2021 ◽  
Vol 2 (3) ◽  
pp. 78-81
Author(s):  
Relizha Yeerlanbieke ◽  
Huazhang Wang
Keyword(s):  
Target Tracking ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Residual Network ◽  
Target Feature ◽  
Data Set ◽  
Original Target ◽  
Detailed Feature ◽  
Current Stage ◽  
Important Structure

Aiming at the current stage of the twin network target tracking algorithm, the tracking target is occluded, the tracking is affected by illumination, and the target's scale change from far to near or from near to far causes tracking failure. This article will optimize and improve from two directions. The twin neural network first uses an adaptive detailed feature extraction, adds a residual network to the twin network, and embeds a detailed feature retention module in each layer, amplifies the changes in the target feature, and retains the important structure of the original target feature Details: Secondly, the introduction of a spatial attention mechanism allows the main branch to pay more attention to the area to be matched, improves the ability to distinguish features, and makes the tracking effect better. In order to verify the effectiveness of this experiment, this experiment was tested on the data set OTB2015. The experiment proved that the proposed algorithm performs better in accuracy and success rate.

scholarly journals Research on Target Tracking Algorithm Based on Siamese Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haibo Pang ◽  
Qi Xuan ◽  
Meiqin Xie ◽  
Chengming Liu ◽  
Zhanbo Li
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Evaluation Criteria ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Temporal Attention ◽  
Tracking Accuracy ◽  
Current Frame ◽  
Tracking Process ◽  
Recognition Ability

Target tracking is a significant topic in the field of computer vision. In this paper, the target tracking algorithm based on deep Siamese network is studied. Aiming at the situation that the tracking process is not robust, such as drift or miss the target, the tracking accuracy and robustness of the algorithm are improved by improving the feature extraction part and online update part. This paper adds SE-block and temporal attention mechanism (TAM) to the framework of Siamese neural network. SE-block can refine and extract features; different channels are given different weights according to their importance which can improve the discrimination of the network and the recognition ability of the tracker. Temporal attention mechanism can update the target state by adjusting the weights of samples at current frame and historical frame to solve the model drift caused by the existence of similar background. We use cross-entropy loss to distinguish the targets in different sequences so that their distance in the feature domains is longer and the features are easier to identify. We train and test the network on three benchmarks and compare with several state-of-the-art tracking methods. The experimental results demonstrate that the algorithm proposed is superior to other methods in tracking effect diagram and evaluation criteria. The proposed algorithm can solve the occlusion problem effectively while ensuring the real-time performance in the process of tracking.

Robust Target Tracking Algorithm Based on Superpixel Visual Attention Mechanism

2019 ◽  
Vol 10 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Jia Hu ◽  
Xiao Ping Fan ◽  
Shengzong Liu ◽  
Lirong Huang
Keyword(s):  
Visual Attention ◽  
Target Tracking ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Complex Environments ◽  
Superpixel Segmentation ◽  
Tracking Algorithms ◽  
Target Template ◽  
Comprehensive Test ◽  
Visual Attention Mechanism

As existing target tracking algorithms are prone to drift under complex environments, the authors propose a tracking algorithm based on superpixel segmentation and visual attention mechanism. The algorithm works on a particle filter framework, by conducting a superpixel segmentation first and then building a model of the visual attention mechanism for saliently mapping. After extracting HOG features of salient regions, the authors compared the HOG feature with the target template at last, in order to locate the target region in the new frame. The proposed algorithm is evaluated on a comprehensive test platform, in which the simulation show that our tracker is more efficacious and more efficient than the previous traditional target tracking algorithms to cope with target drift issue.

Visual recognition and performance prediction of athletes based on target tracking EIA algorithm

2020 ◽  
pp. 1-14
Author(s):  
Dai Xianpeng
Keyword(s):  
Target Tracking ◽  
Visual Recognition ◽  
Feature Recognition ◽  
Tracking Algorithm ◽  
Appearance Model ◽  
Target Feature ◽  
Practical Applications ◽  
Adaptive Fusion ◽  
The Face ◽  
And Performance

In the past, the research of target tracking was often to track problems in a static background, and the tracking scenes were often stable, and the targets were special. However, target tracking is often a tracking problem in the face of realistic complex scenes, and the target and scene are more complex. Therefore, the target tracking algorithm still faces many challenges in practical applications, especially in sports visual feature recognition. Based on the needs of sports feature recognition, this study combines the EIA algorithm to construct a feature recognition model. Moreover, for the shortcomings of the compressed sensing tracking algorithm that cannot accurately and comprehensively describe the target shape through a single target feature, the multi-feature adaptive fusion method is used to visualize the target appearance model, thus improving the accuracy of target tracking. In addition, this study design experiments to analyze the performance of the algorithm model. The research results show that the algorithm model of this study has certain recognition effects.

scholarly journals A Method of Real-temporal Object Tracking Combined the Temporal Information and Spatial Information

2021 ◽  
Author(s):  
XiaoShuo Jia ◽  
Zhihui Li ◽  
Kangshun Li ◽  
Shangyou Zeng
Keyword(s):  
Target Tracking ◽  
Spatial Information ◽  
Tracking Algorithm ◽  
Temporal Information ◽  
Information Network ◽  
Data Set ◽  
Test Set ◽  
Single Target ◽  
Before And After ◽  
Single Target Tracking

Abstract The purpose of single target tracking is to accurately and continuously locate a specific object when it is moving. However, when the objects encounter with fast movement, severe occlusion, too small size, and the same local features, the tracking algorithm which based on correlation filter or convolutional neural network will appear the positioning error phenomenon. Aiming at the above problems, this paper designs a single target tracking algorithm: relative temporal spatial network (RTSnet). RTSnet is a multi-thread network that composed of Relative temporal Information Network (RTInet) and Relative Spatial Information Network (RSInet). RTInet is designed on the basis of LSTM, and it has the predictable characteristics of temporal. It mainly obtains the relative temporal information between the frames before and after the target. RSInet, an improved twin network based on the Triplet Network, has the effect of similarity determination which can to obtain the spatial information between the frames before and after the target. In the experiments, the RTSnet is trained by using LASOT data set and verified by using the LASOT test set and The OTB100 data set. In the test set of LASOT, the accuracy of RTSnet reaches 85.5%, StruckSiam reaches 50% and STRCF reaches 54%. Meanwhile, its tracking speed reaches 120fps due to the RTSnet adopts dual-thread operation. On the OTB100 data-set, the accuracy of RTSnet is 81.1%.

scholarly journals Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhengze Li ◽  
Jiancheng Xu
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Convolutional Neural Network ◽  
Target Tracking ◽  
Network Structure ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Adaptive Tracking ◽  
Target Template ◽  
Overall Performance

With the advent of the artificial intelligence era, target adaptive tracking technology has been rapidly developed in the fields of human-computer interaction, intelligent monitoring, and autonomous driving. Aiming at the problem of low tracking accuracy and poor robustness of the current Generic Object Tracking Using Regression Network (GOTURN) tracking algorithm, this paper takes the most popular convolutional neural network in the current target-tracking field as the basic network structure and proposes an improved GOTURN target-tracking algorithm based on residual attention mechanism and fusion of spatiotemporal context information for data fusion. The algorithm transmits the target template, prediction area, and search area to the network at the same time to extract the general feature map and predicts the location of the tracking target in the current frame through the fully connected layer. At the same time, the residual attention mechanism network is added to the target template network structure to enhance the feature expression ability of the network and improve the overall performance of the algorithm. A large number of experiments conducted on the current mainstream target-tracking test data set show that the tracking algorithm we proposed has significantly improved the overall performance of the original tracking algorithm.

The Study on Weighted Target Tracking Algorithm for Binary Sensor Networks

2010 ◽  
Vol 32 (9) ◽  
pp. 2052-2057
Author(s):  
Xiao-yan Sun ◽  
Jian-dong Li ◽  
Yan-hui Chen ◽  
Wen-zhu Zhang ◽  
Jun-liang Yao
Keyword(s):  
Sensor Networks ◽  
Target Tracking ◽  
Tracking Algorithm ◽  
Binary Sensor

scholarly journals Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network

2020 ◽  
Vol 10 (24) ◽  
pp. 9132
Author(s):  
Liguo Weng ◽  
Xiaodong Zhang ◽  
Junhao Qian ◽  
Min Xia ◽  
Yiqing Xu ◽  
...  
Keyword(s):  
Smart Grids ◽  
Recognition Rate ◽  
Low Frequency ◽  
Attention Mechanism ◽  
Learning Ability ◽  
Residual Network ◽  
Multi Scale ◽  
Energy Disaggregation ◽  
Benchmark Datasets ◽  
Load Disaggregation

Non-intrusive load disaggregation (NILD) is of great significance to the development of smart grids. Current energy disaggregation methods extract features from sequences, and this process easily leads to a loss of load features and difficulties in detecting, resulting in a low recognition rate of low-use electrical appliances. To solve this problem, a non-intrusive sequential energy disaggregation method based on a multi-scale attention residual network is proposed. Multi-scale convolutions are used to learn features, and the attention mechanism is used to enhance the learning ability of load features. The residual learning further improves the performance of the algorithm, avoids network degradation, and improves the precision of load decomposition. The experimental results on two benchmark datasets show that the proposed algorithm has more advantages than the existing algorithms in terms of load disaggregation accuracy and judgments of the on/off state, and the attention mechanism can further improve the disaggregation accuracy of low-frequency electrical appliances.

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