| Title | Reliable abnormal event detection from IoT surveillance systems |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Elbasi, Ersin |
| Conference Name | 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS) |
| Keywords | activity detection, Cameras, feature extraction, house caring, Human Behavior, Internet of Things, machine learning, machine learning algorithms, Metrics, pubcrawl, Resiliency, security, surveillance, video surveillance, Videos |
| Abstract | Surveillance systems are widely used in airports, streets, banks, military areas, borders, hospitals, and schools. There are two types of surveillance systems which are real-time systems and offline surveillance systems. Usually, security people track videos on time in monitoring rooms to find out abnormal human activities. Real-time human tracking from videos is very expensive especially in airports, borders, and streets due to the huge number of surveillance cameras. There are a lot of research works have been done for automated surveillance systems. In this paper, we presented a new surveillance system to recognize human activities from several cameras using machine learning algorithms. Sequences of images are collected from cameras using the internet of things technology from indoor or outdoor areas. A feature vector is created for each recognized moving object, then machine learning algorithms are applied to extract moving object activities. The proposed abnormal event detection system gives very promising results which are more than 96% accuracy in Multilayer Perceptron, Iterative Classifier Optimizer, and Random Forest algorithms. |
| DOI | 10.1109/IOTSMS52051.2020.9340162 |
| Citation Key | elbasi_reliable_2020 |
Reliable abnormal event detection from IoT surveillance systems