Object Activity Recognition System with Shadow Suppression Using Adaptive Gaussian Mixture Model

Moving object detection is an important step in any video surveillance system, tracking or video activity. This paper examines the result of the adaptive Gaussian Mixture Model using the Maximum A posterior (MAP) updates on video clips (dataset) obtained from Adeyemi College of Education Ondo, Nigeria. The results showed a reliable moving object detection algorithm, shadows constitute a problem, in that moving shadows can be mistaken as moving objects. The shadow was suppressed using the HSV and Phong illumination Model. The overall performance of this system was evaluated using the confusion matrix and the receiver operating characteristic (ROC), shadow detection and shadow discrimination values which showed a better result compared to existing benchmarks.