Spatio‐temporal feature fusion based correlative binary relevance for visual object detection

2021 ◽  
Author(s):  
Amaresh M ◽  
Chitrakala S
Keyword(s):  
Object Detection ◽  
Feature Fusion ◽  
Visual Object ◽  
Binary Relevance ◽  
Spatio Temporal ◽  
Temporal Feature

Unmanned vehicle dynamic obstacle detection, tracking and recognition method based on laser sensor

2021 ◽  
Vol 14 (2) ◽  
pp. 239-251
Author(s):  
Hualei Zhang ◽  
Mohammad Asif Ikbal
Keyword(s):  
Feature Fusion ◽  
Obstacle Detection ◽  
Geometric Features ◽  
Recognition Method ◽  
Content Type ◽  
Detection And Tracking ◽  
Spatio Temporal ◽  
Dynamic Obstacle ◽  
Real Vehicle ◽  
Temporal Feature

PurposeIn response to these shortcomings, this paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.Design/methodology/approachThe existing dynamic obstacle detection and tracking methods based on geometric features have a high false detection rate. The recognition methods based on the geometric features and motion status of dynamic obstacles are greatly affected by distance and scanning angle, and cannot meet the requirements of real traffic scene applications.FindingsFirst, based on the geometric features of dynamic obstacles, the obstacles are considered The echo pulse width feature is used to improve the accuracy of obstacle detection and tracking; second, the space-time feature vector is constructed based on the time dimension and space dimension information of the obstacle, and then the support vector machine method is used to realize the recognition of dynamic obstacles to improve the obstacle The accuracy of object recognition. Finally, the accuracy and effectiveness of the proposed method are verified by real vehicle tests.Originality/valueThe paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors. The accuracy and effectiveness of the proposed method are verified by real vehicle tests.

Segmentation of Coronary Arteries Images Using Spatio-temporal Feature Fusion Network with Combo Loss

2021 ◽  
Author(s):  
Hongyan Zhu ◽  
Shuni Song ◽  
Lisheng Xu ◽  
Along Song ◽  
Benqiang Yang
Keyword(s):  
Coronary Arteries ◽  
Feature Fusion ◽  
Spatio Temporal ◽  
Temporal Feature

Multi-agent Deep Reinforcement Learning with Spatio-Temporal Feature Fusion for Traffic Signal Control

2021 ◽  
pp. 470-485
Author(s):  
Xin Du ◽  
Jiahai Wang ◽  
Siyuan Chen ◽  
Zhiyue Liu
Keyword(s):  
Reinforcement Learning ◽  
Feature Fusion ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Multi Agent ◽  
Spatio Temporal ◽  
Temporal Feature

Spatio-temporal feature fusion for dynamic taxi route recommendation via deep reinforcement learning

2020 ◽  
Vol 205 ◽  
pp. 106302 ◽  
Author(s):  
Shenggong Ji ◽  
Zhaoyuan Wang ◽  
Tianrui Li ◽  
Yu Zheng
Keyword(s):  
Reinforcement Learning ◽  
Feature Fusion ◽  
Route Recommendation ◽  
Spatio Temporal ◽  
Temporal Feature

Deep spatio-temporal feature fusion with compact bilinear pooling for multimodal emotion recognition

2018 ◽  
Vol 174 ◽  
pp. 33-42 ◽  
Author(s):  
Dung Nguyen ◽  
Kien Nguyen ◽  
Sridha Sridharan ◽  
David Dean ◽  
Clinton Fookes
Keyword(s):  
Emotion Recognition ◽  
Feature Fusion ◽  
Spatio Temporal ◽  
Multimodal Emotion Recognition ◽  
Temporal Feature

scholarly journals Cascaded Regional Spatio-Temporal Feature-Routing Networks for Video Object Detection

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 3096-3106 ◽  
Author(s):  
Hui Shuai ◽  
Qingshan Liu ◽  
Kaihua Zhang ◽  
Jing Yang ◽  
Jiankang Deng
Keyword(s):  
Object Detection ◽  
Video Object ◽  
Spatio Temporal ◽  
Temporal Feature
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