Unusual activity detection in surveillance video scene : review

Abstract EN

Abnormal activity may indicate threats and risks to others.

An anomaly can be defined as something that deviates from what is expected, common, or normal.

Because it is difficult to continuously monitor public spaces, intelligent video surveillance is necessary, which can monitor human actions in real-time and categorize them as ordinary or exceptional, as well as create an alarm.

Human activities in public and sensitive regions such as bus stations, airports, railway stations, malls, banks, universities, car parks, roads, and other regions can be monitored using visual surveillance to prevent crime, theft, terrorism, vandalism, accidents, and other suspicious activities.

This makes video surveillance a key to increasing public security.

The main objective of event discovery is to discover the occurrence of events and categorize them into normal or abnormal actions.

This discovery requires identifying and tracking objects and then learning what is going around those tracked objects.

Recent research is based on one of two technologies: handcrafted features and deep learning models.

Handmade features are based on extracting low-level features, and their strength is based on selecting the best features, that produce the best results.

After the success of deep learning techniques for classifying images, the researchers examined the ability of deep learning techniques to detect, which bypasses the manual step of feature extraction and works directly with images.

This paper presents a survey of both handmade and deep learning models to detect abnormal events.