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文件名称：Machine+Learning+Methods+for+Behaviour+Analysis+and+Anomaly+Detection-2018.pdf
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更新时间：2021-03-24 16:30:50
Machine Learning 机器学习
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.
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Machine+Learning+Methods+for+Behaviour+Analysis+and+Anomaly+Detection+in+Video-Springer(2018).pdf