A Ground Truth Annotated Video Dataset for Moving Object Detection in Degraded Atmospheric Outdoor Scenes

Moving object detection has been extensively studied during the last few decades. However the detection of moving objects in different degraded atmospheric conditions (i.e. fog, haze, dust and poor illumination) is less understood. This is possibly because of the lack of a suitable and publically-available video dataset under such weather conditions within which salient objects are unambiguously defined and annotated. This paper describes the creation and design of a new video dataset named as “Tripura University Video dataset (TUVD)” which specifically addresses degraded atmospheric weather conditions for moving object detection in outdoor scenes. The objective is to provide video dataset containing moving objects with annotated ground truth in the form of images of the salient objects in the image sequences. Currently, TUVD contains 55 videos of moving objects (vehicles, animals and pedestrian) under degraded atmospheric conditions. Using TUVD a comparison is made between the results of seven existing state-of-the-art visibility enhancement methods. Quantitative assessment of image quality is achieved using four no-reference image based quality assessment metrics. Overall, the most efficient method for visibility restoration of outdoor scenes is found to be one based on multi-scale fusion, although most of the other algorithms tested show interesting capability in specific cases.