Efficient mining of IoT based data streams for advanced computer vision systems

With emergence of Internet of Things (IoT) and subsequent technologies, smart devices are being increasingly used in various domains such as smart homes, smart parking, intelligent transportation etc. Vast amount of image and video data has been produced by IoT based systems in the form of continuous and possibly infinite image and video streams. This demands the development of advanced predictive vision systems which exploits stream mining concepts for intelligent processing of visual data streams. Among other challenges faced by visual data streams, a major challenge is concept drift, which is caused by overtime change in data distribution. In the presence of skewed data, the detection of concept drift becomes more challenging. When analyzing the data generated from smart devices and other performance critical wireless sensors, concept drift affects data integrity and accuracy of prediction results. EWMA for Concept Drift Detection (ECDD) has been proposed in the literature for detecting data streams. However, ECDD has a high prediction error rate which makes it less useful for performance critical data streams generated by imaging and video data streams. In this paper, Vision based Drift Detection Method (VisDDM) is proposed, which systematically handles abrupt and gradual concept drift in data streams. Experiments have been performed using synthetic and real world datasets from different application domains. Our proposed VisDDM algorithm is able to handle abrupt and gradual drift types and outperformed the existing drift detection methods in terms of accuracy and mean evaluation time.