Density Based Multifeature Background Subtraction with Relevance Vector Machine

Background modeling and subtraction is a natural technique for object detection in videos. A pixel wise background modeling and subtraction technique using multiple features is involved in classification. A pixel wise generative background model is obtained for each feature efficiently and effectively by Kernel Density Approximation (KDA). The proposed SVM algorithm is not more robust to shadow, illumination changes, spatial variations of background. Background subtraction and classification is performed in a discriminative manner based on Relevance Vector Machines (RVMs). Approximately the same classification accuracy as SVM is obtained using Relevance Vector Machine-based classification, with a significantly smaller Relevance Vector Rate and, therefore, much faster testing time, compared with Support Vector Machine (SVM) based classification. This feature makes the RVM-based Background modeling and subtraction approach more suitable for Applications that require low complexity and possibly real-time classification.