LAUSR

Moving object detection is an essential step in several computer visions like salient object detection, visual object tracking, and video surveillance etc. Many existing methods have a drawback of low efficiency in the challenging scenes like dynamic background, camera jitter, and bad weather images. In this research, Unified model (Yolov3-Improved Non-Maximum Suppression (INMS)) method is proposed to increases the performance in moving object detection. The Change Detection net (CDNET) dataset was trained and also COCO 2014 and PascalVOC data sets were applied to analyse the performance of the developed model. The experimental analysis shows that the developed method has higher efficiency in detecting objects in camera jitter and dynamic background scene. The performance evaluation has been done using the precision, recall, IoU and the accuracy metrics for the proposed model. The results show that the developed model has the ability to effectively identify multiple objects in the dynamic background, while the existing method has the capacity to identify only single object.