An electronic processing method for monitoring of a race track, comprising: - obtaining of at least one sequence of images (132, 134, 136) from a camera capturing at least one section of the race track; - detecting at least one vehicle in the sequence of images (132, 134, 136) using automatic object recognition; - re-identifying the at least one vehicle as a specific vehicle of a predetermined set of vehicles using embedding, comprising computing at least one reference embedding vector (172, 174) for the embedding based on at least one image taken when the at least one vehicle enters the race track or the monitored part of the race track; - mapping of the re-identified vehicle to a corresponding digital twin in a digital representation of the race track; - determining a first real-world position of the re-identified vehicle based on a mapping relationship and adding first position and timestamp information to the corresponding digital twin each time a first real-world position of a re-identified vehicle is determined, so as to store a track history of the respective vehicle in the digital representation of the race track, wherein the mapping relationship maps a plurality of pixel areas in the images (132, 134, 136) of the at least one sequence of images to a corresponding plurality of real-world positions of the corresponding section of the race track captured by the camera; - detecting of an anomaly along the race track based on a comparison of at least two images (132, 136) of the at least one sequence of images (132, 134, 136); - classifying the detected anomaly as acceptable or inacceptable, wherein a detected anomaly is classified as inacceptable, if it is classified, based on automatic pattern recognition, as vehicle part, oil, and/or gravel; - determining a second real-world position of at least one inacceptable anomaly detected along the race track based on the mapping relationship, and adding second position and timestamp information to a corresponding digital representation of at least one inacceptable anomaly detected along the race track; - correlating the first and second position and timestamp information, so as to identify a vehicle likely to have caused the at least one inacceptable anomaly; and - displaying the position of detected vehicles and anomalies on a visual representation of the race track. - obtaining of at least one sequence of images (132, 134, 136) from a camera capturing at least one section of the race track; - detecting at least one vehicle in the sequence of images (132, 134, 136) using automatic object recognition; - re-identifying the at least one vehicle as a specific vehicle of a predetermined set of vehicles using embedding, comprising computing at least one reference embedding vector (172, 174) for the embedding based on at least one image taken when the at least one vehicle enters the race track or the monitored part of the race track; - mapping of the re-identified vehicle to a corresponding digital twin in a digital representation of the race track; - determining a first real-world position of the re-identified vehicle based on a mapping relationship and adding first position and timestamp information to the corresponding digital twin each time a first real-world position of a re-identified vehicle is determined, so as to store a track history of the respective vehicle in the digital representation of the race track, wherein the mapping relationship maps a plurality of pixel areas in the images (132, 134, 136) of the at least one sequence of images to a corresponding plurality of real-world positions of the corresponding section of the race track captured by the camera; - detecting of an anomaly along the race track based on a comparison of at least two images (132, 136) of the at least one sequence of images (132, 134, 136); - classifying the detected anomaly as acceptable or inacceptable, wherein a detected anomaly is classified as inacceptable, if it is classified, based on automatic pattern recognition, as vehicle part, oil, and/or gravel; - determining a second real-world position of at least one inacceptable anomaly detected along the race track based on the mapping relationship, and adding second position and timestamp information to a corresponding digital representation of at least one inacceptable anomaly detected along the race track; - correlating the first and second position and timestamp information, so as to identify a vehicle likely to have caused the at least one inacceptable anomaly; and - displaying the position of detected vehicles and anomalies on a visual representation of the race track.