Towards to real world vehicle privacy protection: A new dataset and benchmark

The vehicle privacy protection plays a vital role in releasing or sharing of traffic videos. License plate, as the identifiable mark of a vehicle, contains the most sensitive information for a vehicle. Therefore, masking the license plates is a common way to protect the privacy of corresponding vehicles. However, in the real world scenarios, it is often hard to locate the small and shifting license plates, and therefore precise and cost-effective privacy protection is quite challenging. To address this problem in surveillance video, we fully explore all available spatio-temporal cues and design bidirectional Kalman filter model in the consecutive frames to locate missing license plates. To verify effectiveness of the proposed benchmark, we build a new License Plates Privacy-preserving Dataset (LPPD) collected from various scenes with diverse privacy and utility annotations. We demonstrate that our proposed method show very promising capability of privacy protection on the real world dataset without sacrificing its utility.