Intelligent Ocean Governance—Deep Learning-Based Ship Behavior Detection and Application

2021 ◽  
pp. 458-468
Author(s):  
Peng Qin ◽  
Yang Cao
Keyword(s):  
Deep Learning ◽  
Behavior Detection ◽  
Ocean Governance ◽  
Ship Behavior

Motion-shape-based deep learning approach for divergence behavior detection in high-density crowd

2021 ◽  
Author(s):  
Muhammad Umer Farooq ◽  
Mohamad Naufal M. Saad ◽  
Sultan Daud Khan
Keyword(s):  
Deep Learning ◽  
High Density ◽  
Learning Approach ◽  
Behavior Detection

scholarly journals Transfer Deep Learning Along With Binary Support Vector Machine for Abnormal Behavior Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 61085-61095 ◽  
Author(s):  
Ahlam Al-Dhamari ◽  
Rubita Sudirman ◽  
Nasrul Humaimi Mahmood
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Abnormal Behavior ◽  
Support Vector ◽  
Behavior Detection ◽  
Abnormal Behavior Detection

scholarly journals Abnormal Ship Behavior Detection after the Closure of AIS Based on Radar Data

2021 ◽  
Author(s):  
Tao Zhang ◽  
Chuanchang Liu ◽  
Bodong Wen
Keyword(s):  
Reporting System ◽  
Radar Data ◽  
Detection Algorithm ◽  
Trajectory Data ◽  
Behavior Detection ◽  
The Earth ◽  
Illegal Activities ◽  
Abnormal Behaviors ◽  
Influence Of Noise ◽  
Ship Behavior

Abstract In marine transportation, most ships are equipped with AIS devices. The AIS data sent by these devices can help maritime authorities to manage ships in relevant sea areas. However, AIS is a self-reporting system, when a ship is engaged in illegal activities, the AIS device may be turned off. Therefore, after the AIS is closed, if the ship's behavior during a certain period of time is different from the ship's behavior before the closure of AIS, the different behavior is likely to represent that the ship is conducting illegal activities. This behavior is considered abnormal and needs to be detected in time. Based on radar trajectory data, the detection of abnormal ship behavior is studied from two aspects: speed and direction. In order to improve the intelligent level of abnormal ship behavior detection, the abnormal speed behavior detection algorithm combined with rules and clustering (ASBD-RC) and the abnormal direction behavior detection algorithm combined with partition and the earth mover's distance (ADBD-PE) are proposed. The ASBD-RC algorithm can reduce the influence of noise and sea clutter on abnormal speed behavior detection. The ADBD-PE algorithm can effectively partition and identify trajectory segments with abnormal direction. In the experiment, based on the real and simulated radar trajectories, the abnormal behaviors of ships under different scenarios are generated. The experimental results show that in most scenarios, the ASBD-RC algorithm and the ADBD-PE algorithm can effectively detect abnormal ship behavior. And compared with other algorithms, the proposed two algorithms have better and more stable detection results.

scholarly journals Antisocial Online Behavior Detection Using Deep Learning

2019 ◽  
Author(s):  
Elizaveta Zinovyeva ◽  
Wolfgang K. Härdle ◽  
Stefan Lessmann
Keyword(s):  
Deep Learning ◽  
Online Behavior ◽  
Behavior Detection

Violent crowd behavior detection using deep learning and compressive sensing

2019 ◽  
Author(s):  
Mingliang Gao ◽  
Jun Jiang ◽  
Lixiu Ma ◽  
Shuwen Zhou ◽  
Guofeng Zou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Compressive Sensing ◽  
Crowd Behavior ◽  
Behavior Detection

scholarly journals Deep Learning Based Driver Smoking Behavior Detection for Driving Safety

2020 ◽  
Vol 8 (1) ◽  
pp. 15-20
Author(s):  
Tzu-Chih Chien ◽  
◽  
Chieh-Chuan Lin ◽  
Chih-Peng Fan
Keyword(s):  
Deep Learning ◽  
Smoking Behavior ◽  
Driving Safety ◽  
Behavior Detection

Driver Distracted Behavior Detection Technology with YOLO-Based Deep Learning Networks

2021 ◽  
Author(s):  
Yen-Sok Poon ◽  
Ching-Yun Kao ◽  
Yen-Kai Wang ◽  
Chih-Chin Hsiao ◽  
Ming-Yu Hung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Networks ◽  
Behavior Detection ◽  
Detection Technology
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