| Abstract |
Autonomous surveillance has several applications which include surveilling calamity prone areas, search and rescue operations, military operations and traffic management in smart cities. In low visibility conditions like low-light, haze, fog, snowfall, autonomous surveillance is a challenging task and current object detection models perform poorly in these conditions. Lack of datasets that capture challenging low visibility conditions is one of the reasons that limits the performance of currently available models. We propose a synthetic dataset for Human Action Recognition for search and rescue operations consisting of aerial images with different low visibility conditions including low light, haze, snowfall and also images with combinations of these low visibility conditions. We also propose a framework called ExtremeDetector for object detection in extreme low visibility conditions consisting of a degradation predictor and enhancement pool for enhancing a low visibility image and YOLOv5 for object detection in the enhanced image. © 2023 by SCITEPRESS – Science and Technology Publications, Lda. |
