Best Practices for Implementing Intelligent Video Analytics Solutions

In today’s tech-driven era where huge volumes of video content are generated at an unprecedented pace — from various sources such as security cameras, offices, warehouses, carparks and retail stores — reviewing the visual data has become a mammoth challenge.

By leveraging artificial intelligence and machine learning, video analytics enables the automated analysis of video footage to detect patterns, track movement, recognise objects or people, and trigger alerts based on predefined rules. Through this, it transforms passive video recordings into actionable intelligence for effective decision-making.

To fully leverage the potential of video analytics solutions, organisations must follow certain key practices. The following best practices help maximise accuracy, minimise false alerts, and ensure the reliability and scalability of the solutions.

Define Clear Objectives

Before implementing a video analytics solution, it’s crucial to first identify the specific use case. Is the primary goal to enhance security, boost operational efficiency, or gather marketing insights? Clarifying the intended purpose will shape the selection of features—whether that’s real-time people counting, heatmaps for foot traffic analysis, or object detection for safety monitoring. It’s also important to determine whether the system needs to respond in real-time, such as triggering immediate alerts for suspicious activity, or if post-incident analytics are sufficient for reviewing and improving past events.

Additionally, consider how notifications and insights will be delivered—through live dashboards for quick decision-making or detailed reports for ongoing analysis. Finally, set SMART (Specific, Measurable, Achievable, Relevant, Time-bound) goals to track performance and impact. Examples might include reducing security incidents by 25% within six months, improving response times to falls among elderly residents, or enhancing the visitor experience by cutting queue wait times.

Choose the Right Technology Components

When it comes to building a surveillance system, selecting the right type of CCTV camera is essential. Different cameras serve different needs, and understanding their different features ensures you get the most effective surveillance solution. For example, fixed bullet cameras are typically mounted in a fixed position, and they act as a deterrent to potential intruders. They are typically used for monitoring specific, targeted areas such as entrances, ATMs, or hallways. Dome cameras, on the other hand, often come with a wider field of view. Some models even feature vandal-resistant housings, making them ideal for high-traffic or at-risk areas.

Once the right camera type has been chosen, the next step is to determine its management and storage. There are three primary options: edge devices, on-premise servers, and cloud platforms. Edge devices refer to cameras that can process and sometimes store video directly, typically on an SD card. Many modern IP cameras have built-in capabilities such as motion

detection, analytics, and limited onboard storage. Its pros include low latency and reduced bandwidth usage. Its cons include limited storage capacity as well as low scalability.

Locally within a facility, an on-premise setup comprises cameras, a video management system, and a storage system. Solutions like Milestone XProtect and AXIS Camera Station, offered by the Canon Group, fall under this category. These platforms are powerful, offering robust video storage, live monitoring, and advanced video content analytics. They offer full control over video data, high performance and reliability. Maintaining such systems, however, require a higher upfront cost as well as IT expertise and physical space.

Finally, cloud-enabled Video Surveillance as a Service solutions such as Milestone Arcules, offer global accessibility, amazing scalability and multi-site management from one interface. Designed to reduce complexities, they can store videos as well as support Access Control as a Service and offer capabilities such as IoT sensors, forensic video search and AI analytics.

Optimise Data Management

Next is to understand the nature of the video data being captured, and it is important to distinguish between real-time and archived footage. Real-time footage refers to live video streams that are actively monitored or analysed as events unfold. This type of data is crucial in scenarios where immediate response is necessary, such as monitoring for patient falls or break-ins. In contrast, archived footage consists of previously recorded video stored for later retrieval.

The length of the raw video plays a significant role in determining system requirements. Longer or continuous recordings demand substantial storage capacity and efficient processing. To manage the sheer volume of data, compression techniques are applied. Common video compression standards include H.264 and H.265. H.265 (also known as HEVC) is more efficient than H.264, offering the same video quality at nearly half the bit rate, which makes it highly suitable for long-term video storage without sacrificing detail.

As video surveillance systems grow in scale and complexity, managing and retrieving relevant footage becomes increasingly difficult without effective metadata and indexing strategies. Metadata — from object types to facial recognition data and licence plate numbers — can be automatically generated through AI-powered analytics. These systems can tag specific people, vehicles, behaviours, and timestamps in real time or during post-processing. By indexing this metadata, users can conduct rapid, targeted searches across vast video archives without having to manually scrub through footage.

Ensure Privacy and Compliance

Understanding and complying with data privacy regulations is a critical component of any video surveillance strategy. In Singapore, organisations must adhere to the Personal Data Protection Act (PDPA) and the European Union’s General Data Protection Regulation (GDPR). Both regulations have strict stipulations on consent mechanisms, data retention policies, and cross-border data transfer rules.

Regular assessments must be conducted to ensure compliance. This spans from evaluating how video data is collected — in this case CCTVs — to how it is stored and processed. Through these assessments, organisations can identify risks such as unauthorised access to footage, excessive data retention, or unintended surveillance of individuals in private spaces. Mitigation measures may include implementing strict access controls, encrypting video data at rest and in transit, and ensuring data minimisation practices — only collecting what is necessary for a clearly defined purpose.

To support privacy compliance, modern video surveillance systems offer advanced features such as dynamic masking and exclusion zones. Dynamic masking can automatically blur faces or other sensitive areas in real time or during playback, helping to anonymise individuals while preserving situational awareness. Exclusion features allow operators to define specific zones within the camera’s field of view where recording or analytics are disabled, ensuring that areas deemed private or sensitive are not captured.

Edge-based solutions such as Canon's Workplace AI can operate without a video management system when real-time monitoring and alerts are required, while also addressing strict data privacy requirements. During incidents, only event snapshots are recorded, ensuring that unnecessary data is not stored. Childcare centres can benefit greatly from its intelligent privacy masking as well. It protects children’s identities and sensitive areas, all while automating the monitoring of unattended children, falls, fires and unauthorised access.

Work with the Vendor to Continuously Test, Calibrate & Improve

To ensure a surveillance system is effective, it is important to simulate real-world conditions during both day and night. Varying lighting conditions can affect detection accuracy, so testing under different scenarios helps assess system reliability in diverse environments.

Evaluating incident accuracy is also key. Monitoring false positives helps identify if the system is too sensitive or missing critical events. Adjustments can be made to improve precision and reduce unnecessary alerts.

User feedback also plays a vital role in optimization. Gathering insights on usability, latency, and system responsiveness allows teams to refine performance. Feedback loops — where users flag false alerts — help improve AI accuracy over time. Regular staff training ensures operators understand system capabilities and new features. Well-trained personnel are more effective in using tools and responding to alerts accurately.

Finally, to maintain performance, organisations should retrain AI models, upgrade hardware when needed, and install regular software updates. These steps keep the system current, efficient, and responsive to changing needs.

Get in touch with a Canon consultant who can help you assess your surveillance needs, recommend the right solutions, and guide you through implementation for optimal performance and compliance.