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. You can use the advanced search, redaction, and facial recognition analytics to find relevant images and critical information across multiple video files from 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 behaviors of their customers. The software within these applications then makes multiple recommendations about product assortment and placement.
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. Video-analytics applications are capable of much more precise image analysis. Users can program surveillance systems to detect specific visual patterns, such as movements associated with retail theft or the appearance of flames or unauthorized parking.
- Better business insights. With their advanced image-processing capabilities, video-analytics applications can consider multiple visual inputs, some of which may be ambiguous and require careful processing. For instance, they can assess the demographics and behaviors of retail customers and turn this information into business insights that assist with product assortment and placement, potentially improving store efficiency, customer conversion, customer loyalty, and other metrics.
- Access to large data sets and more nuanced analyses. The software algorithms in video-analytics applications are now capable of gathering and analyzing video footage from multiple sources, thereby generating more detailed insights. For example, surveillance applications can identify people based on physical characteristics from video feeds collected at multiple locations at different times. Similarly, retail applications can aggregate data from multiple videos 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. For instance, some cities are examining aggregated data from city and highway video cameras for the first time, looking at volume, timing, and distribution of traffic. This information may help improve traffic management and could even be used when designing future roadways.
Gone are the days where you need to constantly monitor video live footage to detect anomalies. Or, analyze recorded videos for hours to draw some conclusion. With intelligent, advanced video analytics one can get real-time, accurate information for decision making.