An Improved Object Detection Technique for Hazard Avoidance Systems

Abstract

Hazard detection and avoidance at construction sites working with heavy equipment and moving vehicles is one of the biggest issues in modern surveillance. Background subtraction using a Gaussian Mixture Model (GMM) is widely utilized for identification of moving objects with most existing methods leading to improvements but lacking accuracy of object detection. This paper aims to improve accuracy and processing time for object detection. The proposed algorithm consists of a correlation coefficient to reduce the existing geometric error and provide more accurate detection of moving objects by comparing foreground and background pixels in every frame. A Kalman filter is used for keeping track of the object. The results demonstrate that the proposed algorithm outperforms existing applications in terms of accuracy of object detection. On this basis, it is recommended that object detection with a correlation coefficient of background and foreground pixels of objects can be used for hazard detection in real-time monitoring systems such as traffic monitoring and detection and tracking of humans.