A look at how video analytics works, from camera feeds and model training to deep learning, and its expanding scope for solving unpredictable real-world problems.
Video analytics is a dependable, technology-driven approach to identifying and preventing unwanted occurrences. Although the architecture behind specific use cases may differ, the underlying methodology stays consistent, which is what gives the technology such broad scope for solving real-world problems.
How Does Video Analytics Work?
Content can be analysed in two ways: triggering alerts for specific events as they happen, and conducting advanced searches through recorded footage for forensic analysis. Multi-angle camera coverage of a monitored area feeds the system and maximises the effectiveness of the data collected.
Central vs Edge Processing and Model Training
Analysis software can run on central servers or be embedded directly in cameras through edge processing. Defining scenarios and training models involves several tasks, including image classification, localisation, object detection, object identification and object tracking, so the system learns exactly what to look for.
Recent Improvements and Challenges
- Deep learning has enabled automated counting, licence plate recognition, facial recognition and intelligent parking systems.
- Real-time video mining analyses both live feeds and historical data to reveal customer behaviour and traffic violations.
- Problem identification is difficult in unpredictable real-world scenarios.
- Irregular data and the limitations of conventional image processing create implementation obstacles.
Conclusion
Evolving software increasingly relies on AI and deep neural networks that mimic human behaviour, as seen in optical character recognition, to handle non-standardised datasets. The future demands customisable parameterisation so complete solutions can adapt to ever more complex scenarios such as smart homes.