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Machine Learning and Video Surveillance

Recent advancements in technology have paved the way for many new developments in the security industry. Video surveillance is turning a corner in terms of how it can capture incidents and monitor buildings. Using video surveillance for the mere purpose of obtaining video records post incident, is not as productive as it once was. Companies like icetana are using AI to completely transform the value of video surveillance into a multifaceted risk mitigation tool. Furthermore, it directly effects the efficiency of the video surveillance equipment, as it is capable of performing several functions at once including detection, prevention, environmental learning and liability protection.

Machine Learning

Machine learning technology is the main driver of the movement from forensic to live video. This surveillance tool is made possible through motion detection and environment learning technology. Furthermore, it is a technology that learns its own environment so that it can constantly monitor designated areas for anomalies. This differs from forensic video as it allows for automatic detection of incidents and instantaneous alerts of irregular occurrences. The machine learning technology completely eliminates the need for a security personal to monitor the security cameras in real time.

Risk Mitigation

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Video surveillance is a crucial component in buildings security efforts. It is without doubt, that the numerous cameras residing in facilities produce large amounts of footage daily. This means that in the case of an incident, several hours of footage needs to be examined. This method is not only a unproductive use of time but does not prevent the occurrence from potentially escalating. Subsequently this creates a demand for AI driven surveillance that can help building managers control a vast area without the need for a security team monitoring live footage. With the help of AI surveillance, irregular activity will be automatically flagged and an alert will be sent out. This allows building managers to mitigate the situation as soon as it arises as well as easily find the flagged footage. This is important as it can save the stakeholders from potential risk, by catching incidents before they have a chance to escalate. To conclude, risk mitigation is at the forefront of the movement and holds the capacity to transform how incidents are being detected and mitigated.


A noteworthy benefit to AI video is the versatile nature of this surveillance method. The machine learning technology can detect anomalies like a human would, first identify abnormal movement in a specific area and secondly alert the security team to take care of the situation. From learning its own environment to surveilling 24/7, AI videos efficient nature provides a greater return on your investment in surveillance equipment. The money invested in the equipment goes further as it can protect against potential liability as well as crime prevention. By automating surveillance, it eliminates human errors, decreases the need for physical security, all while increasing the efficiency of the security measures. Investing in security systems that will not only mitigate foreseeable risks but also combine and automate mundane tasks is what the future of security looks like.

In conclusion, the automation of threat detection works to make managing buildings safer and more efficient. Utilizing artificial intelligence is a great way to incorporate innovative efforts and can assist in transforming the security industry into a more efficient and secure future. Video surveillance can exemplify the effect AI technology has on security and can show case how the growth of machine learning can help the security industry evolve.

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