Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter
Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter
复制标题
通过多视图学习和小波滤波器使用集成框架进行鲁棒视觉船舶跟踪
DOI：
10.3390/s20030932
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发表时间：
2020-02-01
期刊：
影响因子：
3.9
通讯作者：
Xiong, Pengwen
中科院分区：
文献类型：
--
作者：
Chen, Xinqiang;Chen, Huixing;Xiong, Pengwen
海事监控视频提供重要的现场动态交通信息(交通量、船速、航向等)。针对不同的交通参与者(海事监管部门、船员、船东等)这极大地有利于海上态势自动感知和海上安全改善。传统的模型在很大程度上依赖于视觉舰船特征，以便从可能包含任意跟踪振荡的海上图像序列中跟踪舰船。针对这一问题，我们提出了一种基于多视点学习算法和小波滤波模型的集成船舶跟踪框架。该模型首先根据序贯重要性抽样规则，使用粒子滤波对候选船舶进行抽样。其次，我们提出了一种多视点学习算法，通过提取一组不同的舰船轮廓相关特征(即高斯拉普拉斯、局部二值模式、Gabor滤波、方向梯度直方图和Canny描述子)和联合利用每种舰船轮廓特征之间的潜在关系来学习高层固有舰船特征，从而获得原始的船舶跟踪结果。第三，在小波滤波的帮助下，我们执行了数据质量控制程序，以识别船位中的异常振荡，并对其进行进一步校正，生成最终的船舶跟踪结果。我们通过四个海上监控视频演示了所提出的船舶跟踪器在典型海上交通场景中的性能。
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.