Activity recognition and localization on a truck parking lot

In this paper we present a set of activity recognition and localization algorithms that together assemble a large amount of information about activities on a parking lot. The aim is to detect and recognize events that may pose a threat to truck drivers and trucks. The algorithms perform zone-based activity learning, individual action recognition and group detection. Visual sensor data, from one camera, have been recorded for 23 realistic scenarios of different complexities. The scene is complicated and causes uncertain and false position estimates. We also present a situational assessment ontology which serves the algorithms with relevant knowledge about the observed scene (e.g. information about objects, vulnerabilities and historical data). The algorithms are tested with real tracking data and the evaluations show promising results. The accuracies are 90 % for zone-based activity learning, 71 % for individual action recognition and 66 % for group detection (i.e. merging of people).