Abstract:
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....Show MoreMetadata
Abstract:
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).
Published in: 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance
Date of Conference: 27-30 August 2013
Date Added to IEEE Xplore: 21 October 2013
Electronic ISBN:978-1-4799-0703-8