A Comprehensive Guide: Considerations for leveraging Intelligence Video Analytics (IVA)
With the global video analytics market size projected to grow from $8.37 billion in 2023 to $37.55 billion by 2030 and the explosion of both point solutions hardware and creators of algorithms entering the market, how do enterprises ensure you are getting business value longevity?
AI-infused Video Analytics potential is here. The ability to interpret and respond to complex scenarios in a manner that could solve long-term business problems. From recognising behavioural patterns to spotting object anomalies in real-time, the capabilities are virtually boundless. The ability to sift through reams of footage with accuracy and speed seems like a no-brainer but there are careful considerations needed when determining the beginning of the adoption of IVA technologies and the end state that aligns to your corporate strategies.
In recent years, the advent of Artificial Intelligence (AI) has transformed numerous industries, and video analytics is no exception. Organisations across sectors are increasingly turning to AI-powered video analytics to extract valuable insights, enhance security, and optimise operations. However, as with any powerful technology, leveraging Intelligent Video Analytics (IVA) requires careful consideration across several components.
This guide explores key factors to weigh up when considering the implementation of IVA, from uncovering its potential limitations to navigating ethical and privacy considerations and ensuring long-term business value is realised.
1. What Are the Potential Hurdles?
Before diving into Intelligent Video Analytics (IVA), it's essential to understand that this is going to be an iterative journey, but one which can deliver significant value back to an organisation when done well. While AI algorithms excel at processing vast amounts of data, identifying patterns, detecting events and delivering critical business insights, they may initially struggle with accuracy in complex field of view (FoV) environments or under challenging conditions such as low lighting, occlusions and variability in camera quality. Additionally, AI models require ongoing training and optimisation to maintain performance levels. Organisations must be aware of these possible limitations and set realistic initial expectations and invest in the ongoing optimisation of their IVA capabilities including the fine tuning of FoV settings, enabling active learning within deployed AI models and the ongoing monitoring and management of model drift.
2. Navigating Ethical and Privacy Imperatives
Ethical AI and privacy considerations are paramount when deploying IVA solutions. Surveillance technologies raise concerns about data privacy, consent, and the potential for misuse. The CSIRO’s AI Ethics Principles report provides organisations with an extensive guide to help navigate the use of responsible AI, below is a summary of some of the key principles to consider.
3. Data Privacy and Consent
Ensure compliance with data privacy regulations such as GDPR, CCPA, and other relevant laws governing the collection, processing, and storage of personal data. Implement measures to anonymise or pseudonymise personally identifiable information to protect individuals' privacy rights.
4. Surveillance and Monitoring
Transparently communicate the purpose and scope of surveillance or monitoring activities enabled by the video analytics solution. Clearly define the boundaries of surveillance zones and ensure that data collection is proportional to the intended use case.
5. Bias and Fairness
Mitigate biases in the AI algorithms used for video analytics to ensure fair and equitable treatment of individuals from diverse backgrounds. Regularly assess and audit the performance of AI models to identify and address potential biases related to factors such as race, gender, age, or socio-economic status.
6. Data Security and Integrity
Implement robust security measures to protect video data from unauthorized access, disclosure, or tampering. Encrypt sensitive data both in transit and at rest and restrict access to authorized personnel only. Implement secure authentication mechanisms, access controls, and audit trails to monitor and track data usage.
7. Accountability and Governance
Establish clear accountability structures and governance mechanisms to oversee the deployment and operation of the video analytics solution. Designate roles and responsibilities for data stewardship, compliance, and risk management. Implement policies, procedures, and controls for ethical decision-making, risk assessment, and incident response.
By prioritising these ethical and privacy considerations, organisations can deploy AI-based video analytics solutions responsibly, protect individuals' privacy rights, and build trust with stakeholders. By embedding ethical principles into the design, deployment, and operation of video analytics systems, organisations can harness the benefits of AI technology while mitigating potential risks and fostering a culture of responsible innovation.
8. A Platform Approach vs. Point Solutions
When considering AI video analytics solutions, organisations must choose between a point solution and a platform approach. While point solutions may address an immediate specific use case, a platform approach offers several advantages. A Platform should not be tied to a use case, it should have the ability to integrate multiple capabilities such as video processing, machine learning, and real-time analytics into a unified framework, providing scalability, flexibility, and comprehensive insights. By adopting a platform approach, organisations can future-proof their investments, streamline operations, and derive greater value from their video data.
Comprehensive Integration
Platform Approach: A platform integrates various components such as video processing, analytics engines, AI algorithms, and application development tools into a unified framework. This seamless integration facilitates data flow and interoperability across different modules, enabling organisations to leverage the full spectrum of IVA capabilities.
Point Solution: Point solutions typically address specific use cases or functionalities in isolation, this can lead to fragmented systems and data silos. Integrating multiple-point solutions can be complex and costly, requiring additional resources for implementation maintenance and customisation.
Holistic Insights
Platform: By consolidating various IVA capabilities into a single platform, organisations can gain holistic insights from their video data. They can analyse data across AI models to maximise event detection accuracy, identify new correlations, and derive actionable intelligence more effectively compared to relying on disparate point solutions.
Point Solution: Point solutions focus on specific use cases or functionalities, providing limited insights into broader trends or patterns within the data. Organisations may miss out on opportunities for cross-functional analysis and strategic decision-making.
Cost Efficiency
Platform: While the initial investment in a platform may be higher than implementing individual point solutions, platforms offer economies of scale and lower total cost of ownership over time. They eliminate the need for multiple licenses, reduce integration costs, and streamline maintenance and support efforts.
Point Solution: Implementing multiple point solutions can lead to higher costs associated with licensing, integration, customization, and ongoing maintenance. Organisations may also incur hidden costs related to data duplication, inefficiencies, and vendor lock-in.
Future Proofing
Platform: Platforms are designed to evolve with technological advancements, industry trends, and changing business needs. They provide a foundation for innovation, allowing organisations to easily incorporate new video streams, deploy new best in class AI models and define new features and detection events to meet new and changing business requirements.
Point Solution: Point solutions may become obsolete or inadequate as business requirements evolve or new technologies emerge. Organisations risk being left behind or facing costly migrations if their point solutions cannot adapt to future challenges.
In conclusion, while point solutions may offer specific functionality and are an appropriate choice for single-use case environments, a platform approach provides a more comprehensive, scalable, and future-proof solution for IVA. By embracing a platform approach, organisations can unlock the full potential of their video data, drive innovation, and stay ahead in an increasingly competitive landscape.