Moving Object Extraction through a Real-World Variable-Bandwidth Network using KFDA-based RBF

Motion detection has become one of the most important applications in traffic monitoring systems. Video communication in traffic monitoring systems may suffer network congestion or unstable bandwidth over real-world networks with definite bandwidth, which is dangerous in motion detection in video streams of variable-bit-rate. In this paper, we propose a unique Kernel Fisher’s linear discriminant (KFLD)-based radial basis function (RBF) network for motion detection approach for accurate and complete detection of moving objects in video streams of both high and low bit rates. The proposed method will be accomplished through a combination of two stages: pattern generation (PG) and motion extraction (ME). In the PG stage, the variable bitrate video stream properties will be accommodated by this new technique, which subsequently distinguishes the moving objects within the segmented regions belonging to the moving object class by using two devised procedures that is Background Discriminant Procedure and Object Extraction Procedure during the ME stage. The accuracy result evaluations can show that the new method exhibits superior when compared to the old methods.