Integrating color and motion to enhance human detection within aquatic environment

An adaptive spatio-temporal filtering scheme based on a novel concept of motion frequency is proposed as the preprocessing step to enhance human detection under a noisy aquatic environment. In this framework, each pixel is first classified into one of three categories quantified by its motion frequency, each of which is filtered using an appropriate filtering scheme. For regions affected by glistening reflections and glares, a color compensation filter is specially developed to improve partly hidden human detection and minimize errors due to moving background elements. Additionally, a blob-based verification procedure is introduced to remove the target´s shadow. Experimental results demonstrate the effectiveness of the algorithm and its role in enhancing the robustness of an aquatic surveillance system for outdoor swimming pools at nighttime