Dianhai Wang

Vision-Based Moving Objects Detection with Background Modeling

Video detection has become an efficient technique support for collecting parameters of urban traffic. Detection of moving objects with background model in complex environment is developed in this paper. 1) In order to obtain moving objects from the video sequence efficiently, a background initialization algorithm based on clustering classifier is presented, all stable non-overlapping intervals in the temporal training sequence of each pixel are located as possible backgrounds by slip window; then the background interval is obtained from the classified data set of possible backgrounds by unsupervised clustering. 2) According to spatial-temporal property of pixels, the paper also presents Mixture Gaussian background update algorithm based on object-level with moving segmentation. The method can get over the effect of object’s long-term stop. The proposed approach is validated under real traffic scenes. Experimental results show that moving objects detection is robust and adaptive, can be well applied in real-world.

Research on Variable Lane Signalized Control Method

Taking the variable lane as a research object, the paper designs the detector layout plan according the traffic flow characteristics. Then, it determines the invariable lanes attribute according to the detector data and proposes the signalized control method of invariable lanes. In order to avoid the traffic flow confusion at approaches because of the drivers entering into the wrong traffic lane when the lane’s attribute is changed, paper lays out a fictitious stop line and proposed a pre-signal control method based on the fictitious stop line. The suggested strategy was verified with simulation software VISSIM. The results showed that this article method may enhance the road intersection traveling capability and reduces the delay by comparing with different channelization and control methods.

Nighttime Vehicle Detection Based on Illumination Visibility Recognition

Video detection is a valuable application in intelligence transportation systems. Some common traffic flow parameters like volume, velocity and vehicle category could be estimated automatically by video detection in real-time. At present, most of Video detection systems focus on algorithms designed and applied for day-time conditions. In this paper, a visual-based vehicle detection method for nighttime conditions is developed. Firstly, we consider there were two kinds of traffic scenes at nighttime: lamp scene and non-lamp scene. We calculate the illumination visibility of the region of interest (ROI) background image to divide traffic scenes into two predefined categories based on Support Vector Machine (SVM). Then, Different algorithms are used to complete the vehicle detection for two night scenes. At last, we test our approach with several video sequences from realistic traffic scenes including lamp scenes and non-lamp scenes. Experimental results show good performance of vehicle detection for nighttime conditions.