tailieunhanh - Báo cáo hóa học: " Moving object detection using keypoints reference model"

Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa hoc quốc tế đề tài : Moving object detection using keypoints reference model | Wan Zaki et al. EURASIP Journal on Image and Video Processing 2011 2011 13 http content 2011 1 13 D EURASIP Journal on Image and Video Processing a SpringerOpen Journal RESEARCH Open Access Moving object detection using keypoints reference model Wan Mimi Diyana Bt. Wan Zaki Aini Hussain and Mohamed Hedayati Abstract This article presents a new method for background subtraction BGS and object detection for a real-time video application using a combination of frame differencing and a scale-invariant feature detector. This method takes the benefits of background modelling and the invariant feature detector to improve the accuracy in various environments. The proposed method consists of three main modules namely modelling matching and subtraction modules. The comparison study of the proposed method with a popular Gaussian mixture model proved that the improvement in correct classification can be increased up to 98 with a reduction of false negative and true positive rates. Beside that the proposed method has shown great potential to overcome the drawback of the traditional BGS in handling challenges like shadow effect and lighting fluctuation. 1. Introduction Today every state-of-the-art security system must include smart video systems that act as remote eyes and ensure the security and safety of the environment. One of the main challenges in any visual surveillance systems is to identify objects of interest from the background. Background subtraction BGS is the most widely used technique for object detection in real-time video application 1 2 . There are various approaches in BGS modelling. Running Gaussian average RGA 3 Gaussian mixture model GMM 4 5 kernel density estimation 6 and median filtering 7 8 are the most common methods due to their reasonable accuracy and speed. Although all these techniques work moderately well under simple conditions because they treat each pixel independently without considering its neighbouring area their .