Abstract
Until recently, dashboard cameras have been used primarily by law enforcement agencies around the world. With their widespread use among private users, they have recently become almost ubiquitous. Driven by the potential of their application, especially in the insurance and security industries, this rise has been contributing to the advancement of research in the fields of video analytics, leading to the development of new anomaly detection techniques [1]. In this paper we exploit the potential of the Google Video Intelligence APIs in order to perform a standard object tracking operation on front/rear videos produced by car DVRs. This technology allowed us to save the intervals in which the detection occurred, used for subsequent data alignment, a very under-researched problem in this field. Our new approach for alignment is based on calculating the period of time that objects in the video are outside the field of view of both dashcams, using the.gpx file attached to each pair of front/rear videos. after this was accomplished, the individual detections were compared by constructing a fictitious continuous interval from the rcalculated blind time. the results differ according to the traffic levels within the videos. the best performances were achieved in low traffic situations where the calculation of the blind time interval is considerably more accurate, given the nearly constant speed held by the dashcam mounted car.
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Balzano, W., Barolli, L., Zangrillo, F. (2022). Object Tracking by Google Cloud API and Data Alignment for Front/rear Car DVR Footages. In: Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2021. Lecture Notes in Networks and Systems, vol 343. Springer, Cham. https://doi.org/10.1007/978-3-030-89899-1_9
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