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Video Analytics Technology and Internet of Things

These days video analytics technology is transforming the Internet of Things and creating new opportunities across various industry verticals. This is a technology that is allowing cameras to recognize people, objects, and situations automatically and applies machine-learning algorithms to video feeds. These applications are relatively new, but there are many factors that are encouraging their growth, including the increased sophistication of analytical algorithms and lower costs for hardware, software, storage and ease of implementation.

Intelligent video analytics helps security and public safety organizations develop comprehensive security, intelligence and investigative capabilities using video. Through facial recognition analytics one can find relevant images and critical information about it across multiple video files and multiple camera types. Selected live-streaming cameras plus pre-recorded video ingestion from both fixed cameras and cameras in motion can be supported.

Such advancement has helped business executives recognize the value of video analytics across sectors, from city planning to healthcare, traffic monitoring system to crime detection, manufacturing to public safety. Retailers, for instance, are using IoT applications with video analytics to assess the age range, demographic profile, and behaviours of their customers. Based on this they can design/redesign the layout of the store or select right products to display.

Some of the key features:

  • Real-time processing:  This feature allows users to see evidence of potential problems as soon as it is available and take immediate corrective action, such as deploying store personnel to monitor shoplifters.
  • Greater accuracy: Precise analysis can be done for visual patterns, such as retail theft related movement or unauthorised parking or the appearance of flames can be detected.
  • Better business insights: Video-analytics applications can consider multiple visual inputs, some of which may be ambiguous and require careful processing. It can access demographic and behaviour of retail customers and use them for generating business insights. Thus, assisting product placement and assortment and customer conversion, loyalty, store efficiency and other metrics.
  • Access to large data sets and more nuanced analyses: Video footage can be gather analysed for detailed insights. Based on physical characteristic from video feeds, surveillance application can identify people. Similarly, retail applications can aggregate data from multiple video feeds to determine the shopping patterns characteristic of different demographic groups.
  • More innovative use cases: With better video-analytics applications, new use cases are emerging. Various traffic data can be aggregated to find out the traffic data, timing, volume & distribution of traffic and further help improving traffic management and designing future roadways.

Gone are the days where you need to constantly monitor video live footage to detect anomalies. Or, analyse recorded videos for hours to draw some conclusion. With intelligent, advanced video analytics one can get real-time, accurate information for decision making.

Reference: www.mckinsey.com