Beginning Anomaly Detection Using

 Video  Surveillance

Anomaly detection also has uses in video surveillance, where anomaly detection 

software can monitor video feeds and help flag any videos that capture anomalous 

action. While this might seem dystopian, it can certainly help catch criminals or 

maintain public safety on busy streets or in cities. For example, this software could 

identify a mugging in a street at night as an anomalous event and alert authorities who 

can call in police officers. Additionally, it can detect unusual events at crossroads such as 

an accident or some unusual obstruction and immediately call attention to the footage.

 Summary

Generally, anomaly detection is utilized heavily in medicine, finance, cybersecurity, 

banking, networking, transportation, and manufacturing, but it is not just limited 

to those fields. For nearly every case imaginable involving data collection, anomaly 

detection can be put to use to help users automate the process of detecting anomalies 

and possibly removing them. Many fields in science can utilize anomaly detection 

because of the large volume of raw data collection that goes on. Anomalies that would 

interfere with the interpretation of results or otherwise introduce some sort of bias into 

the data could be detected and removed, provided that the anomalies are caused by 

systematic or random errors.

In this chapter, we discussed what anomalies are and why detecting anomalies can 

be very important to the data processing we have at our organizations.

In the next chapter, we will look at traditional statistical and machine learning 

algorithms for anomaly detection.

Chapter 1   What Is anomaly DeteCtIon?

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© Sridhar Alla, Suman Kalyan Adari 2019 

S. Alla and S. K. Adari, Beginning Anomaly Detection Using Python-Based Deep Learning,  

https://doi.org/10.1007/978-1-4842-5177-5_2