Vehicle Detection and Classification in Urban Intersection Based on Kalman Filtering Algorithm
QI Yonggan · Video Engineering · 2013
To provide in advance information to relevant authorities on the road traffic demand,an urban intersection vehicle detection and classification system based on kalman filter is designed to detect,count and classify the passed vehicles. Firstly,background subtraction and Kalman filter algorithm are used to detect and track individual vehicles throughout the detection zone. Then,the detected vehicles blob-area is used to trigger the segmentation unit which in turn extracts the vehicle while at a point closest to the camera. Finally,both geometric and appearance features of the segmented vehicles are passed to the LDA classifier for proper categorisation. The effectiveness of proposed system verifies on video sequence with 3 400 frames taken in by self.Experimental results show that the system achieves a high counting performance of 97. 44% with corresponding classification rate of 88. 0% and it has better detection performance comparing with the latest approaches.