AI camera mistakes guitar for rider in Bengaluru, fined for not wearing helmet
AI enforcement camera error guitar as pillion river slap helmet less riding guys. | Photo credit: Special arrangement
An artificial intelligence (AI) traffic camera installed to detect traffic violations in Bengaluru recently issued a routine helmet violation citation to a not-so-common defaulter: a guitar case riding on its owner’s back.
Souvik Dutta was traveling on his scooter from Hebbal towards the Tin Factory with a guitar strapped to his back. The AI camera apparently mistook the guitar case for the chainsaw rider and incorrectly identified him as a person traveling without a helmet. The challan along with the photo captured by the camera was subsequently sent to Mr. Dutta’s wife who owns the scooter.
He lodged a complaint on the official handle of the Bengaluru Traffic Police (BTP) and said, “I was riding my wife’s two wheeler alone with a guitar slung on my back in Bangalore. @blrcitytraffic, my wife received a traffic challan (notice no. 86202541) for ‘pillion riding without helmet violation!’ chalslan”
In response to his post, BTP officials asked him to contact the traffic automation section to rectify the violations.
Limitations on AI Monitoring
The incident highlighted the limitations of automated transportation systems. Many motorists have expressed concerns on social media that the enforcement cameras are recording them for signal jumping offences, even if they only cross a crosswalk slightly to allow other vehicles that have a green signal to pass.
A senior traffic police officer admitted that the driver’s reported violation was a false flag caused by an error in judgment by the artificial intelligence system, which happens occasionally. Sometimes they even flag down a small child sitting between two people on a scooter to ride without a helmet, he said. Such violations can be challenged, he said, after which they are verified and corrected.
“The AI system is getting sharper with more and continuous data feed and it can go wrong at times. We already have a team that usually checks and updates such mislabels. It could be one oversight and we will fix it,” he said, admitting that the system has not yet reached 100% accuracy.
Published – 26 Sep 2026 21:40 IST