Chen Xinqiang, Chen Huixing, Wu Huafeng, Huang Yanguo, Yang Yongsheng, Zhang Wenhui, Xiong Pengwen
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China.
Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China.
Sensors (Basel). 2020 Feb 10;20(3):932. doi: 10.3390/s20030932.
Maritime surveillance videos provide crucial on-spot kinematic traffic information (traffic volume, ship speeds, headings, etc.) for varied traffic participants (maritime regulation departments, ship crew, ship owners, etc.) which greatly benefits automated maritime situational awareness and maritime safety improvement. Conventional models heavily rely on visual ship features for the purpose of tracking ships from maritime image sequences which may contain arbitrary tracking oscillations. To address this issue, we propose an ensemble ship tracking framework with a multi-view learning algorithm and wavelet filter model. First, the proposed model samples ship candidates with a particle filter following the sequential importance sampling rule. Second, we propose a multi-view learning algorithm to obtain raw ship tracking results in two steps: extracting a group of distinct ship contour relevant features (i.e., Laplacian of Gaussian, local binary pattern, Gabor filter, histogram of oriented gradient, and canny descriptors) and learning high-level intrinsic ship features by jointly exploiting underlying relationships shared by each type of ship contour features. Third, with the help of the wavelet filter, we performed a data quality control procedure to identify abnormal oscillations in the ship positions which were further corrected to generate the final ship tracking results. We demonstrate the proposed ship tracker's performance on typical maritime traffic scenarios through four maritime surveillance videos.
海上监视视频为不同的交通参与者（海上监管部门、船员、船东等）提供了关键的现场运动交通信息（交通流量、船速、航向等），这对自动海上态势感知和海上安全改善大有裨益。传统模型严重依赖视觉船舶特征，以便从可能包含任意跟踪振荡的海上图像序列中跟踪船舶。为了解决这个问题，我们提出了一个带有多视图学习算法和小波滤波器模型的集成船舶跟踪框架。首先，所提出的模型按照顺序重要性采样规则，使用粒子滤波器对船舶候选对象进行采样。其次，我们提出一种多视图学习算法，分两步获得原始船舶跟踪结果：提取一组不同的船舶轮廓相关特征（即高斯拉普拉斯算子、局部二值模式、伽柏滤波器、方向梯度直方图和坎尼描述符），并通过联合利用每种船舶轮廓特征所共享的潜在关系来学习高级内在船舶特征。第三，在小波滤波器的帮助下，我们执行了一个数据质量控制程序，以识别船舶位置中的异常振荡，并对其进行进一步校正，以生成最终的船舶跟踪结果。我们通过四个海上监视视频展示了所提出的船舶跟踪器在典型海上交通场景中的性能。
