As road injuries are one of the most common causes of death, tremendous efforts are made worldwide to reduce the number of hazardous situations and collisions in traffic. Shared information about the locations and intentions of road users has the potential to improve traffic safety, efficiency and convenience. Transportation is becoming increasingly connected, with revolutionary developments in road infrastructure and vehicle technology. Future improvements are also required for separately detecting overlapping road users. Car headlights and long dark shadows were found especially difficult for the motion detection, which caused incorrect bounding boxes. The most impactful deficiency of MoDeCla was errors in bounding box placement. Compared to state-of-the-art object detectors, MoDeCla performed detection an order of magnitude faster, yet achieved similar accuracy. Separate datasets were gathered during winter and summer, enabling comparison of the detectors in significantly different weather conditions. To validate the applicability of MoDeCla in intelligent transportation applications, a detection benchmark was carried out on manually labelled data gathered from surveillance cameras overseeing urban areas in Espoo, Finland. The approach is computationally lightweight and capable of running in real-time on an inexpensive single-board computer. We propose utilising Motion Detection and Classification (MoDeCla) for road user detection. Similar problems are faced in many intelligent transportation applications, in which road users are detected with a roadside camera. However, accurate object detection algorithms are typically computationally heavy, depending on delay-prone cloud computation or expensive local hardware. Computer vision can be utilised to detect road users, conveying their presence to vehicles that cannot perceive them. With the emerge of intelligent and connected transportation systems, driver perception and on-board safety systems could be extended with roadside camera units.
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