Detail‐enhanced target segmentation method for thermal video sequences based on spatiotemporal parameter update technique

Thermal images have been widely used for detection, tracking, and classification of targets at night for military purpose. A thermal imaging sensor receives the radiation energy from the target and the background, so it has advantages in night vision. However, the thermal images have lower spatial resolution and more blurred edges than colour images, and edges can be contaminated by flames. Therefore, the extracted edges which can be used in automatic target classification may contain inaccurate edges that can decrease the accuracy of the classification. In this study, to overcome these problems, a novel background modelling method based on spatiotemporal parameter updating technique and foreground detection method using intensity moments analysis is proposed. To validate the proposed method, thermal images with military vehicle targets (tank and infantry vehicle) and public dataset in CDNET2014 ( www.changedetection.net ) are used in subjective and objective tests, respectively. The experiment results showed that the proposed method outperformed the state‐of‐the‐art methods in objective measures and in subjective measure especially on detail‐enhanced segmentation.