The importance of detecting anomalies in surveillance camera data cannot be overemphasized. With the increasing availability of surveillance cameras in public and private locations, the need for reliable and effective methods to detect anomalous behavior has become critical to public safety. Anomaly detection algorithms can help identify potential threats in real time, allowing for rapid intervention and prevention of criminal activity. The examples of anomalies that can be detected by analyzing surveillance camera data include suspicious loitering or lingering, unattended bags or packages, crowd gatherings or dispersals, trespassing or unauthorized access, vandalism or property damage, violence or aggressive behavior, abnormal traffic patterns, missing or abducted persons, unusual pedestrian behavior, environmental anomalies. Detecting these anomalies in surveillance camera data can enable law enforcement, security personnel, and other relevant authorities to respond quickly and effectively to potential threats, ultimately contributing to a safer environment for all. 

Surveillance camera data contains a large amount of information that is difficult for humans to analyze in real time. In addition, the sheer volume of data generated by surveillance cameras makes manual analysis impractical. Therefore, the development of automated anomaly detection algorithms is crucial for effective and efficient surveillance. The goal of this master's thesis is to detect anomalies using video cameras with an embedded machine learning processor and video analytics, such as human behavior. For this purpose, the most appropriate machine learning techniques will be selected and after comparing the results of these techniques, the best anomaly detection technique for the given circumstances will be identified. 

To gather the evidence needed to answer the research questions, I will use a combination of methods appropriate to the study design. The study will follow a mixed-methods approach, combining a systematic literature review (SLR) and a formal experiment. 

In this study, we investigated the effectiveness of various machine learning algorithms in detecting anomalous human behavior in video surveillance data.