What can public traffic cameras reveal? A short horizon privacy audit using open source vehicle tracking

Alam, M. S., Bazilinskyy, P.

Submitted for publication.

ABSTRACT Public traffic cameras are often treated as low risk because they rarely expose clear faces or licence plates. We study a different leakage channel: the behavioural structure extracted from ordinary public traffic footage using open source computer vision. Our preliminary pipeline converts timestamped public YouTube livestream footage from one traffic camera, spanning a 319.64 hour wall clock period, into route based vehicle events and measures how coarse signatures affect confusability. In the current run, 877,704 raw tracks were filtered into 135,488 vehicle events with full wall clock alignment. Using only class, route, and half hour time bin produced 231 baseline signatures and 178 low confusability events. Adding approximate size, speed, duration, and coarse colour increased the signature space to 11,466 signatures and 14,382 low confusability events, with 5,160 rare recurrence candidate signatures. The analysis does not perform person tracking, licence plate recognition, or exact vehicle re identification. Instead, weak, non explicit cues can make public traffic events more distinctive than aggregate counts suggest.