This paper proposes an original moving ship detection approach in video surveillance systems, especially con- centrating on occlusion problems among ships and vegetation using context information. Firstly, an over- segmentation is performed to divide and classify by SVM (Support Vector Machine) segments into water or non-water, while exploiting the context that ships move only in water. We assume that the ship motion to be characterized by motion saliency and consistency, such that each ship distinguish itself. Therefore, based on the water context model, non-water segments are merged into regions with motion similarity. Then, moving ships are detected by measuring the motion saliency of those regions. Experiments on real-life surveillance videos prove the accuracy and robustness of the proposed approach. We especially pay attention to testing in the cases of severe occlusions between ships and between ship and vegetation. The proposed algorithm outperforms, in terms of precision and recall, our earlier work and a proposal using SVM-based ship detection.
In port surveillance, video-based monitoring is a valuable supplement to a radar system by helping to detect smaller ships in the shadow of a larger ship and with the possibility to detect nonmetal ships. Therefore, automatic video-based ship detection is an important research area for security control in port regions. An approach that automatically detects moving ships in port surveillance videos with robustness for occlusions is presented. In our approach, important elements from the visual, spatial, and temporal features of the scene are used to create a model of the contextual information and perform a motion saliency analysis. We model the context of the scene by first segmenting the video frame and contextually labeling the segments, such as water, vegetation, etc. Then, based on the assumption that each object has its own motion, labeled segments are merged into individual semantic regions even when occlusions occur. The context is finally modeled to help locating the candidate ships by exploring semantic relations between ships and context, spatial adjacency and size constraints of different regions. Additionally, we assume that the ship moves with a significant speed compared to its surroundings. As a result, ships are detected by checking motion saliency for candidate ships according to the predefined criteria. We compare this approach with the conventional technique for object classification based on support vector machine. Experiments are carried out with real-life surveillance videos, where the obtained results outperform two recent algorithms and show the accuracy and robustness of the proposed ship detection approach. The inherent simplicity of our algorithmic subsystems enables real-time operation of our proposal in embedded video surveillance, such as port surveillance systems based on moving, nonstatic cameras.
This paper presents an automatic ship detection approach for video-based port surveillance systems. Our approach combines context and motion saliency analysis. The context is represented by the assumption that ships only travel inside a water region. We perform motion saliency analysis since we expect ships to move with higher speed compared to the water flow and static environment. A robust water detection is first employed to extract the water region as contextual information in the video frame, which is achieved by graph-based segmentation and region-based classification. After the water detection, the segments labeled as non-water are merged to form the regions containing candidate ships, based on the spatial adjacency. Finally, ships are detected by checking motion saliency for each candidate ship according to a set of criteria. Experiments are carried out with real-life surveillance videos, where the obtained results prove the accuracy and robustness of the proposed ship detection approach. The proposed algorithm outperforms a state-of-the-art algorithm when applied to the same sets of surveillance videos.