Thresholds Based Image Extraction Schemes in Big Data Environment in Intelligent Traffic Management
Yanxiao Liu, Ching‐Nung Yang, Qindong Sun
Abstract
Video traffic monitoring is an inexpensive and convenient source of traffic data. Traffic images processing are widely used to check traffic conditions and they can determine traffic control strategies in intelligent transportation systems (ITS). However, these traffic images always contain privacy-related data, such as vehicles registration numbers, human faces. Misuse of such data is a threat to the privacy of vehicles divers, passengers, pedestrians, etc. This paper proposes a thresholds-based images extraction solution for ITS. At first, a Faster Region Convolutional Neural Networks (RCNN) model is used to segment a traffic image into multi-regions with different importance levels; then, multi-threshold image extraction schemes are designed based on progressive secret image sharing schemes to extract images contain key traffic information, such as reg number, human faces, in which the region with higher importance level requires higher threshold for extraction. For different roles in ITS, they can extract images with different details, which can protect privacy and anonymity. The proposed methods provide a safe and intelligent way to extract images that can be used for further analysis in ITS.