Video Analytics Market worth $20.3 billion by 2027, growing at a CAGR of 23.4% Report by MarketsandMarkets™

Chicago, June 28, 2023 (GLOBE NEWSWIRE) -- The global Video Analytics Market size is expected to grow from USD 7.1 billion in 2022 to USD 20.3 billion by 2027 at a Compound Annual Growth Rate (CAGR) of 23.4% during the forecast period, according to a new report by MarketsandMarkets™.The growing need among enterprises to leverage BI and actionable insights for advanced operations is expected to drive market growth in the coming years. 

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309 - Tables
67 - Figures
321 - Pages

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Scope of the Report

Over the past few years, a rapid increase in disruptive and innovative technologies, such as Artificial Intelligence (AI), cloud computing, and edge computing in the video analytics landscape, has been observed. AI-based facial expression analysis helps to predict suspicious and abnormal activities. Along with predictive analytics, AI technology reduces false alarms by filtering out noises, such as tree movement, animal movement, and lighting changes. Cloud-based video analytics reduces the overall costs as the storage space, and computing efforts are distributed over the cloud server. Edge-based video analytics helps to process the data on-premises, thus reducing bandwidth and cloud loads while accelerating the analysis process. 

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By Component, the software segment is estimated to account for a larger market share in 2022. Video analytics software enables organizations to capture and analyze critical data and make business decisions using intelligently analyzed data. It has further eliminated the need for manual intervention and continuous human monitoring. Video analytics software also provides real-time alerts during emergencies. 

By Type, the Edge-based segment is expected to grow at a higher CAGR during the forecast period. In the edge-based architecture, video analytics is embedded into the camera and the video there itself. Advancements in deep learning and its integration with the edge system are expected to drive its adoption in the coming years. 

By Vertical, the government & defense segment is expected to account for the largest share in the market. The government & defense vertical, which includes city surveillance and border security, is a major concern for every country. Video analytics in city surveillance is used for controlling traffic on highways or freeways, tracking data from remote locations, managing the crowd flow, detecting motion for identifying over-speeding vehicles, facial recognition by law enforcement authorities, and identifying camera tampering. While in border security it helps governments to secure their borders against illegal immigrants, smuggling, and terrorist attacks.

With the increasing demand for security and non-security applications, facial recognition is gaining traction in the video analytics market. Facial recognition enables the biometric authentication and identification of individuals by comparing their facial traits from a video feed stored in the database. Facial recognition applications are witnessing a high demand for deployment in devices, such as smartphones, laptops, tablets, and personal digital assistants, for security purposes. Various verticals such as retail, media, and entertainment are utilizing facial recognition-equipped CCTVs for inspection and survey purposes, thus contributing to the demand for facial recognition applications. For marketing purposes, billboards are designed with integrated software to identify potential customers' gender, age, and ethnicity for targeted advertising. 

Top Trends in Global Video Analytics Market 

• Artificial Intelligence (AI) and Machine Learning (ML): AI and machine learning are playing an important role in enhancing video analytics capabilities. These technologies enable the creation of sophisticated video analytics platforms that can analyse massive volumes of video data, recognise patterns, and make real-time choices.
• Deep Learning: Deep learning techniques, which are a type of AI, are rapidly being used in video analytics. Deep learning algorithms can learn and extract complicated features from video data automatically, boosting object recognition, behaviour analysis, and anomaly detection accuracy.
• Edge Computing: Edge computing is the processing and analysis of video data at the network's edge, closer to the source. Organisations may minimise latency, improve security, and make quick choices by executing video analytics at the edge, rather than relying primarily on cloud connectivity.
• Video Surveillance and Security: In video surveillance and security applications, video analytics is widely employed. Advanced video analytics technologies can detect and track objects automatically, recognise faces, identify aberrant behaviour, and issue alarms in the event of a security breach.
• Privacy and Data Protection: With the growing use of video analytics, privacy and data security have become major problems. Efforts are being undertaken to provide privacy-enhancing technologies that maintain regulatory compliance while also allowing for effective video analytics.

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Key Industry Development

• Behavior Analysis: AI-powered video analytics can discover anomalies or questionable activity by analysing human behaviour patterns. This technology is often utilised in security applications, where it may detect behaviours such as loitering, unauthorised access, or hostile behaviour and send notifications to security staff.
• Facial Recognition: Video analytics systems can now identify persons from video footage thanks to substantial advancements in facial recognition algorithms. This has a wide range of applications, including law enforcement, access control, and customer identification in retail settings.
• Video Content Understanding: ML systems can now better analyse the content of video footage. They are capable of recognising specific objects, situations, or events and categorising video content accordingly. Applications such as automated video labelling, content recommendation, and video search are made possible by this feature.
• Object Recognition and Tracking: People, cars, and specific objects of interest may now be reliably identified and tracked in real time by video analytics systems. This allows for applications such as traffic monitoring, crowd control, and security monitoring.
• Deep Learning and Neural Networks: Deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have transformed video analytics by allowing for more precise object detection, tracking, and recognition. These sophisticated algorithms can learn from massive volumes of labelled video data, gradually boosting performance.

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