Machine+Learning+Methods+for+Behaviour+Analysis+and+Anomaly+Dete...

Intelligent video systems and analytics represent an active research field combining methods from computer vision, machine learning, data mining, signal processing and other areas for mining meaningful information from raw video data. The availability of cheap sensors and need for solving intelligent tasks facilitate the growth of interest in this area. Vast amount of data collected by different devices require automatic systems for analysis. These systems should be able to make decisions without human interruption or with minimal assistance from a human operator. Video analytics systems should understand and interpret a scene, detect motion, classify and track objects, explore typical behaviours and detect abnormal events [1]. The application area of such systems is huge: preventing crimes in public spaces such as airports, railway stations, or schools; counting objects at stadiums or shopping malls; detection of breaks or leaks; smart homes for elderly people maintenance with fall detection functionality and others. Behaviour analysis and anomaly detection are essential parts of intelligent video systems [2,3]. The objectives of anomaly detection are to detect and inform about any unusual, suspicious and abnormal events happening within the observed scene. These may be pedestrians crossing a road in a wrong place, cars running on the red light, abandoned objects, a person fall, a pipe leak and others. Decisions made by a system should be interpretable by a human therefore the system should also provide information about typical behaviours to confirm its decisions. This thesis develops machine learning methods for automatic behaviour analysis and anomaly detections in video. The methods allow to extract semantic patterns from data. These patterns can be interpreted as behaviours and they are used as a basis for decision making in anomaly detection.