Video-based automatic incident detection (AID) systems are increasingly being used in intelligent transportation systems (ITS). Video-based AID is a promising method of incident detection. However, the accuracy of video-based AID is heavily affected by environmental factors such as shadows, snow, rain, and glare. This paper presents a review of the different work done in the literature to detect outdoor environmental factors, namely, static shadows, snow, rain, and glare. Once these environmental conditions are detected, they can be compensated for, and hence, the accuracy of alarms detected by video-based AID systems will be enhanced. Based on the presented review, this paper will highlight potential research directions to address gaps that currently exist in detecting outdoor environmental conditions. This will lead to an overall enhancement in the reliability of video-based AID systems and, hence, pave the road for more usage of these systems in the future. Last, this paper suggests new contributions in the form of new suggested algorithmic ideas to detect environmental factors that affect AID systems accuracy.

Video-based automatic incident detection (AID) systems are increasingly being used in intelligent transportation systems (ITS). Video-based AID is a promising method of incident detection. However, the accuracy of video-based AID is heavily affected by environmental factors such as shadows, snow, rain, and glare. This paper presents a review of the different work done in the literature to detect outdoor environmental factors, namely, static shadows, snow, rain, and glare. Once these environmental conditions are detected, they can be compensated for, and hence, the accuracy of alarms detected by video-based AID systems will be enhanced. Based on the presented review, this paper will highlight potential research directions to address gaps that currently exist in detecting outdoor environmental conditions. This will lead to an overall enhancement in the reliability of video-based AID systems and, hence, pave the road for more usage of these systems in the future. Last, this paper suggests new contributions in the form of new suggested algorithmic ideas to detect environmental factors that affect AID systems accuracy.