scholarly journals Discriminative Siamese Tracker Based on Multi-Channel-Aware and Adaptive Hierarchical Deep Features

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2329
Author(s):  
Huanlong Zhang ◽  
Rui Duan ◽  
Anping Zheng ◽  
Jie Zhang ◽  
Linwei Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Tracking ◽  
Background Information ◽  
Discriminative Ability ◽  
Siamese Network ◽  
Hierarchical Features

Most existing Siamese trackers mainly use a pre-trained convolutional neural network to extract target features. However, due to the weak discrimination of the target and background information of pre-trained depth features, the performance of the Siamese tracker can be significantly degraded when facing similar targets or changes in target appearance. This paper proposes a multi-channel-aware and adaptive hierarchical deep features module to enhance the discriminative ability of the tracker. Firstly, through the multi-channel-aware deep features module, the importance values of feature channels are obtained from both the target details and overall information, to identify more important feature channels. Secondly, by introducing the adaptive hierarchical deep features module, the importance of each feature layer can be determined according to the response value of each frame, so that the hierarchical features can be integrated to represent the target, which can better adapt to changes in the appearance of the target. Finally, the proposed two modules are integrated into the Siamese framework for target tracking. The Siamese network used in this paper is a two-input branch symmetric neural network with two input branches, and they share the same weights, which are widely used in the field of target tracking. Experiments on some Benchmarks show that the proposed Siamese tracker has several points of improvement compared to the baseline tracker.

Classification and grading of diabetic retinopathy images using mixture of ensemble classifiers

2021 ◽  
pp. 1-13
Author(s):  
R. Bhuvaneswari ◽  
S. Ganesh Vaidyanathan
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Blood Vessels ◽  
Support Vector ◽  
Network Architectures ◽  
Ensemble Classifiers ◽  
Feature Maps ◽  
Class Labels ◽  
Hierarchical Features

Diabetic Retinopathy (DR) is one of the most common diabetic diseases that affect the retina’s blood vessels. Too much of the glucose level in blood leads to blockage of blood vessels in the retina, weakening and damaging the retina. Automatic classification of diabetic retinopathy is a challenging task in medical research. This work proposes a Mixture of Ensemble Classifiers (MEC) to classify and grade diabetic retinopathy images using hierarchical features. We use an ensemble of classifiers such as support vector machine, random forest, and Adaboost classifiers that use the hierarchical feature maps obtained at every pooling layer of a convolutional neural network (CNN) for training. The feature maps are generated by applying the filters to the output of the previous layer. Lastly, we predict the class label or the grade for the given test diabetic retinopathy image by considering the class labels of all the ensembled classifiers. We have tested our approaches on the E-ophtha dataset for the classification task and the Messidor dataset for the grading task. We achieved an accuracy of 95.8% and 96.2% for the E-ophtha and Messidor datasets, respectively. A comparison among prominent convolutional neural network architectures and the proposed approach is provided.

Feature Fusion Video Target Tracking Method Based on Convolutional Neural Network

2020 ◽  
Vol 57 (4) ◽  
pp. 041502
Author(s):  
刘美菊 Liu Meiju ◽  
曹永战 Cao Yongzhan ◽  
朱树云 Zhu Shuyun ◽  
杨尚奎 Yang Shangkui
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Tracking ◽  
Feature Fusion ◽  
Tracking Method ◽  
Video Target Tracking

scholarly journals Attention-mechanism-based tracking method for intelligent Internet of vehicles

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880594 ◽  
Author(s):  
Xu Kang ◽  
Bin Song ◽  
Jie Guo ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Sensor ◽  
Attention Mechanism ◽  
Vehicle Tracking ◽  
Background Region ◽  
Internet Of Vehicles ◽  
Tracking Method ◽  
Siamese Network ◽  
Selection Of

Vehicle tracking task plays an important role on the Internet of vehicles and intelligent transportation system. Beyond the traditional Global Positioning System sensor, the image sensor can capture different kinds of vehicles, analyze their driving situation, and can interact with them. Aiming at the problem that the traditional convolutional neural network is vulnerable to background interference, this article proposes vehicle tracking method based on human attention mechanism for self-selection of deep features with an inter-channel fully connected layer. It mainly includes the following contents: (1) a fully convolutional neural network fused attention mechanism with the selection of the deep features for convolution; (2) a separation method for template and semantic background region to separate target vehicles from the background in the initial frame adaptively; (3) a two-stage method for model training using our traffic dataset. The experimental results show that the proposed method improves the tracking accuracy without an increase in tracking time. Meanwhile, it strengthens the robustness of algorithm under the condition of the complex background region. The success rate of the proposed method in overall traffic datasets is higher than Siamese network by about 10%, and the overall precision is higher than Siamese network by 8%.

scholarly journals Pose Recognition of 3D Human Shapes via Multi-View CNN with Ordered View Feature Fusion

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1368
Author(s):  
Hui Wang ◽  
Peng He ◽  
Nannan Li ◽  
Junjie Cao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Shape Analysis ◽  
Feature Fusion ◽  
Compact Representation ◽  
Pose Recognition ◽  
Siamese Network ◽  
Human Pose

Rapid pose classification and pose retrieval in 3D human datasets are important problems in shape analysis. In this paper, we extend the Multi-View Convolutional Neural Network (MVCNN) with ordered view feature fusion for orientation-aware 3D human pose classification and retrieval. Firstly, we combine each learned view feature in an orderly manner to form a compact representation for orientation-aware pose classification. Secondly, for pose retrieval, the Siamese network is adopted to learn descriptor vectors, where their L2 distances are close for pairs of shapes with the same poses and are far away for pairs of shapes with different poses. Furthermore, we also construct a larger 3D Human Pose Recognition Dataset (HPRD) consisting of 100,000 shapes for the evaluation of pose classification and retrieval. Experiments and comparisons demonstrate that our method obtains better results than previous works of pose classification and retrieval on the 3D human datasets, such as SHREC’14, FAUST, and HPRD.

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