Video-Based Detection Infrastructure Enhancement for Automated Ship Recognition and Behavior Analysis

• Published in: Journal of advanced transportation Vol. 2020; no. 2020; pp. 1 - 12
• Main Authors: Wu, Huafeng, Postolache, Octavian, Luo, Qiang, Yang, Yongsheng, Qi, Lei, Chen, Xinqiang, Tang, Jinjun
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Artificial neural networks; Automation; Behavior; Confidence intervals; Deep learning; Detectors; Efficiency; Feature extraction; Infrastructure; Infrastructure (Economics); Kinematics; Methods; Neural networks; Onsite; Production planning; Spatial analysis; Spatial discrimination; Surveillance; Traffic capacity; Traffic flow; Traffic information; Traffic safety; Transportation; Vehicles; China
• DOI: 10.1155/2020/7194342
• ISSN: 0197-6729

Video-based detection infrastructure is crucial for promoting connected and autonomous shipping (CAS) development, which provides critical on-site traffic data for maritime participants. Ship behavior analysis, one of the fundamental tasks for fulfilling smart video-based detection infrastructure, has become an active topic in the CAS community. Previous studies focused on ship behavior analysis by exploring spatial-temporal information from automatic identification system (AIS) data, and less attention was paid to maritime surveillance videos. To bridge the gap, we proposed an ensemble you only look once (YOLO) framework for ship behavior analysis. First, we employed the convolutional neural network in the YOLO model to extract multi-scaled ship features from the input ship images. Second, the proposed framework generated many bounding boxes (i.e., potential ship positions) based on the object confidence level. Third, we suppressed the background bounding box interferences, and determined ship detection results with intersection over union (IOU) criterion, and thus obtained ship positions in each ship image. Fourth, we analyzed spatial-temporal ship behavior in consecutive maritime images based on kinematic ship information. The experimental results have shown that ships are accurately detected (i.e., both of the average recall and precision rate were higher than 90%) and the historical ship behaviors are successfully recognized. The proposed framework can be adaptively deployed in the connected and autonomous vehicle detection system in the automated terminal for the purpose of exploring the coupled interactions between traffic flow variation and heterogeneous detection infrastructures, and thus enhance terminal traffic network capacity and safety.