Abstract
Recognizing dynamic patterns based on visual processing is significant for many applications. In this paper dynamic texture recognition focuses on outdoor scenarios where a crisis event might occur (i.e. fire in a forest, floods/flooding etc.) Real outdoor scenes may include the objects with dynamic behaviour due to illumination, blurring, or weather conditions effects. Under bad weather conditions the imaging systems is degraded and produce low visibility images. In this work precipitation artefacts and lightning effects for dynamic texture analysis were studied. Experimental results show that the proposed method of weather and adverse lighting effects compensation is feasible and effective for videobased dynamic texture analysis under bad weather conditions.
Highlights
Nowadays dynamic textures (DT) recognition plays an important role in different computer vision community tasks in a variety of fields such as urban and forest scenes
After adverse lighting and weather effects compensation algorithm work true recognition of DT regions increases to 94,3%
This accuracy provides the influence of adverse lighting and weather effects on a quality of DT detection is studied
Summary
Nowadays dynamic textures (DT) recognition plays an important role in different computer vision community tasks in a variety of fields such as urban and forest scenes. Under bad weather conditions the imaging systems is degraded and produce low visibility images. Such effects may significantly degrade the performance of outdoor vision systems which relies on image/video. The removal of these effects provides accuracy increasing of video based computer vision algorithms. The dynamic scenes analysis in video is a very useful task especially for the detection and monitoring of natural hazards such as floods and fires The analysis of such videos is considered of utmost importance during natural disasters, since it can improve situational awareness by providing early detection of floods and fires to prevent or reduce damage from such emergencies. In this paper we target the problem of classifying such DTs as water, smoke and flame
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