How to approach detection of anomalies in a video feed?

1. What are the three 3 basic approaches to anomaly detection?
2. What is a good way to detect anomalies?
3. What is video anomaly detection?
4. How do you detect and identify anomalies in data?

What are the three 3 basic approaches to anomaly detection?

There are three main classes of anomaly detection techniques: unsupervised, semi-supervised, and supervised. Essentially, the correct anomaly detection method depends on the available labels in the dataset.

What is a good way to detect anomalies?

How to detect Anomalies? Simple statistical techniques such as mean, median, quantiles can be used to detect univariate anomalies feature values in the dataset. Various data visualization and exploratory data analysis techniques can be also be used to detect anomalies.

What is video anomaly detection?

Open Set Video Anomaly Detection (OpenVAD) aims to identify abnormal events from video data where both known anomalies and novel ones exist in testing. Unsupervised models learned solely from normal videos are applicable to any testing anomalies but suffer from a high false positive rate.

How do you detect and identify anomalies in data?

The most basic method for detecting data abnormalities is identifying data points that differ from typical statistical features of a distribution, such as mean, median, mode, and quantiles. Assume that an anomalous data point deviates from the mean by a particular standard deviation.