Anomaly Detection and Identification Using Visual Techniques in Streaming Video

Abstract

there are many intelligent systems and tools which uses highly efficient processing models to identify different anomalies with high accuracy. The anomaly detection is of high importance and mostly will come as an absolute requirement at high risk environments and situations. The amount of processing involved in quick decision taking systems bare high deployment costs which restricts the anomaly detection only to a selected few who are capable of building such resource centered systems. Modern world uses drones and other video feeds in order to find and keep track of any anomalous events around a specific area. But most such detection requires absolute manual attention as well as processing power to keep up with real time detection and recognition. The proposed research solution aims to automate this process and includes a two-step anomaly detection system which gives a quicker anomaly detection in an average processing unit time with an advanced recognition model with up to 90% accuracy. The deep learning model (VGG 16) together with alert system and comparison techniques on videos leads into unsupervised anomaly detection of a landscape. The system generates alerts and recognizes anomalies on the alerted video frames. The proposed solution can also be used by any source and does not require high capacity of capability system to get the optimal output. Moreover, the solution brings a simple yet sophisticated technique to address modern anomaly detection and quick alerting system.