• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.22 no.2
• /
• pp.133-146
• /
• 2023

Traffic states in urban areas are essential to implement effective traffic operation and traffic control. However, installing traffic sensors on numerous road sections is extremely expensive. Accordingly, estimating the traffic state using a vehicle-mounted camera, which shows a high penetration rate, is a more effective solution. However, the previously proposed methodology using object tracking or optical flow has a high computational cost and requires consecutive frames to obtain traffic states. Accordingly, we propose a method to detect vehicles and lanes by object detection networks and set the region between lanes as a region of interest to estimate the traffic density of the corresponding area. The proposed method only uses less computationally expensive object detection models and can estimate traffic states from sampled frames rather than consecutive frames. In addition, the traffic density estimation accuracy was over 90% on the black box videos collected from two buses having different characteristics.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.4
• /
• pp.303-308
• /
• 2011

Vehicle black boxes that have similar functions as airplane black boxes are currently being used due to the loss of many lives and properties arising from vehicle accidents. Both black-box products and Event Data Recorder(EDR) systems are currently available in the market. Most of the existing in-vehicle black boxes, however, record only external videos and images and cannot show the vehicle's driving status, whereas EDR products record only the driving status and not external videos. To address the problem of black boxes that can record only videos and images and that of EDR systems that can record only driving data, an integrated vehicle state and video recording system that uses MOST(Media-oriented System Transport) and OBD-II(Onboard Diagnostics II) and CAM (camera) and GPS (global positioning system).

• Journal of Broadcast Engineering
• /
• v.19 no.6
• /
• pp.798-809
• /
• 2014

Video stabilization is performed to remove unexpected shaky and irregular motion from a video. It is often used as preprocessing for robust feature tracking and matching in video. Typical video stabilization algorithms are developed to compensate motion from surveillance video or outdoor recordings that are captured by a hand-help camera. However, since the vehicle video contains rapid change of motion and local features, typical video stabilization algorithms are hard to be applied as it is. In this paper, we propose a novel approach to compensate shaky and irregular motion in vehicle video using linear regression model and vertical projection histogram matching. Towards this goal, we perform vertical projection histogram matching at each sub region of an input frame, and then we generate linear regression model to extract vertical translation and rotation parameters with estimated regional vertical movement vector. Multiple binarization with sub-region analysis for generating the linear regression model is effective to typical recording environments where occur rapid change of motion and local features. We demonstrated the effectiveness of our approach on blackbox videos and showed that employing the linear regression model achieved robust estimation of motion parameters and generated stabilized video in full automatic manner.