scholarly journals DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking

2014 ◽  
Author(s):  
Hanxi Li ◽  
Yi Li ◽  
Fatih Porikli
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Visual Tracking ◽  
Feature Representations ◽  
Discriminative Feature

Robust Visual Tracking Based on Convolutional Neural Networks and Conformal Predictor

2017 ◽  
Vol 37 (8) ◽  
pp. 0815003 ◽  
Author(s):  
高 琳 Gao Lin ◽  
王俊峰 Wang Junfeng ◽  
范 勇 Fan Yong ◽  
陈念年 Chen Niannian
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Visual Tracking

scholarly journals Auditory Inspired Convolutional Neural Networks for Ship Type Classification with Raw Hydrophone Data

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 990 ◽  
Author(s):  
Sheng Shen ◽  
Honghui Yang ◽  
Junhao Li ◽  
Guanghui Xu ◽  
Meiping Sheng
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Filter Banks ◽  
Auditory Filter ◽  
Feature Representations ◽  
Proposed Model ◽  
Energy Pooling ◽  
Practical Guidelines ◽  
Frequency Components ◽  
Underwater Acoustic Signal

Detecting and classifying ships based on radiated noise provide practical guidelines for the reduction of underwater noise footprint of shipping. In this paper, the detection and classification are implemented by auditory inspired convolutional neural networks trained from raw underwater acoustic signal. The proposed model includes three parts. The first part is performed by a multi-scale 1D time convolutional layer initialized by auditory filter banks. Signals are decomposed into frequency components by convolution operation. In the second part, the decomposed signals are converted into frequency domain by permute layer and energy pooling layer to form frequency distribution in auditory cortex. Then, 2D frequency convolutional layers are applied to discover spectro-temporal patterns, as well as preserve locality and reduce spectral variations in ship noise. In the third part, the whole model is optimized with an objective function of classification to obtain appropriate auditory filters and feature representations that are correlative with ship categories. The optimization reflects the plasticity of auditory system. Experiments on five ship types and background noise show that the proposed approach achieved an overall classification accuracy of 79.2%, which improved by 6% compared to conventional approaches. Auditory filter banks were adaptive in shape to improve accuracy of classification.

scholarly journals INVESTIGATION OF CONVOLUTIONAL NEURAL NETWORKS FOR VISUAL TRACKING OF PEDESTRIANS.

2018 ◽  
Vol 6 (5) ◽  
pp. 668-674
Author(s):  
Artūras Jonkus ◽  
◽  
Paulius Tumas ◽  
Artūras Serackis ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Visual Tracking
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