Water Recognition and Segmentation in the Environment Using a Spatiotemporal Approach

In the environment, water is present in a wide variety of scenes, and its detection enables a broad range of applications such as outdoor/indoor surveillance, maritime surveillance, scene understanding, content-based video retrieval, and automated driving as for unmanned ground and aerial vehicles. However, the detection of water is a delicate issue since it can appear in quite varied forms. Moreover, it can adopt simultaneously several textural, chromatic and reflectance properties. The diversification of water appearances makes their classification in the same class a quite difficult task using the general dynamic texture recognition methods. In this work, we propose a new more appropriate approach for recognition and segmentation of water in videos. In this approach, we start with a preprocessing phase in which we homogenize the aspect of the different aquatic surfaces by eliminating any differences in coloration, reflection and illumination. In this phase, a pixel-wise comparison is introduced leading to a unidirectional binarization. Two segmentation steps are then deployed: preliminary segmentation and final segmentation. In the first segmentation, candidate regions are first generated and then classified by applying our spatiotemporal descriptor. This descriptor investigates both spatial and temporal behavior of textures on a local scale through a sliding window. Secondly, a superpixel segmentation is applied in order to regularize the classification results. The proposed approach has been tested using both the recent Video Water and the DynTex databases. Furthermore, it has been compared with other similar work, and with other dynamic texture and material recognition methods. The obtained results show the efficiency of the proposed approach over the other methods. Additionally, its low computational cost makes it suitable for real-time applications.