Qimei Chen

An Automatic Calibration Method for PTZ Camera in Expressway Monitoring System

Camera vision calibration is the key technology of Video-based Traffic Monitoring System (VTMS). PTZ camera is used more and more widely in the VTMS for its high flexibility and mobility which also bring great difficulties in its calibration. This paper presented an automatic method of calibration for PTZ camera used in Expressway Monitoring System so that the system can implement calibration without any manual intervention as soon as the operator changes the situation of the camera. A novel model of traffic monitoring camera is given in the beginning, a theoretical framework is established, aimed at computing all the unknown parameters in the model. Test results using real data are described. Both accuracy and speed are reported. Implementation ways and steps of practical application in the project are given.

A nonparametric approach to foreground detection in dynamic backgrounds

Foreground detection is a fundamental step in visual surveillance. However, accurate foreground detection is still a challenging task especially in dynamic backgrounds. In this paper, we present a nonparametric approach to foreground detection in dynamic backgrounds. It uses a history of recently pixel values to estimate background model. Besides, the adaptive threshold and spatial coherence are introduced to enhance robustness against false detections. Experimental results indicate that our approach achieves better performance in dynamic backgrounds compared with several approaches.

A Non-Parametric Motion Model for Foreground Detection in Camera Jitter Scenes

Foreground detection in video sequences is a fundamental step of extracting information in many visual surveillance applications. However, accurate foreground detection is obstructed by the vibration of cameras. In this letter, we present a non-parametric motion model for foreground detection in camera jitter scenes. More specifically, by studying the distinction between dynamic information of unstable background and that of moving foreground in camera jitter scenes, we find that the distribution of dynamic information of unstable background is relatively constant while that of moving foreground is uncertain. Inspired by this distinction, we propose to model the distribution of dynamic information of unstable background with a non-parametric estimation technique. Then a pixel is detected as foreground if its dynamic information is different from the reference model. Experimental results indicate that the proposed method achieves better performance in foreground detection in camera jitter scenes compared with several methods in the literature, especially those detecting foreground only with color distributions.