Annotation and Benchmarking of a Video Dataset under Degraded Complex Atmospheric Conditions and Its Visibility Enhancement Analysis for Moving Object Detection

Abstract

Detection of moving objects in outdoor environments is an extremely researched topic. However, studies on moving object detection in complex atmospheric/weather conditions are limited, primarily because of the absence of any relevant benchmark dataset. To address this disparity, we introduce a novel benchmark video dataset entitled “Extended Tripura University Video Dataset (E-TUVD)” which is a diverse dataset of complex atmospheric/weather conditions. Currently, E-TUVD is the largest video dataset for moving object detection under degraded atmospheric/weather conditions. The dataset comprises 147 video clips spanning 1-5 minutes in duration of each video clips. Because of the requirement of evaluating any object detection model, this study emphasizes on generation of ground-truth images of salient moving objects on E-TUVD. Using this dataset, we assessed the performance of several state-of-the-art algorithms, considering both the ability to detect moving objects and visibility enhancement under such complex conditions. The method with the best performance was used to investigate the effectiveness of visibility enhancement of atmospheric/weather degraded image sequences for accurate moving object detection. Results and analysis reveal that effective enhancement can significantly improve the ability of detection algorithms under degraded atmospheric/weather conditions to resemble the true properties of moving objects in terms of pixel oriented binary masks.