Significant advancements in video monitoring standards underscore the evolving needs within the security sector.
Last year, the International Electrotechnical Commission (IEC) released a major update to its monitoring standard, IEC 62676-4:2025, replacing the existing DORI framework with the more comprehensive OODPCVS classification. This development reflects rapid advancements in digital imaging and analytics, which have necessitated more detailed and applicable classifications for security applications.
Transition from DORI to OODPCVS
The new OODPCVS framework emerged in response to substantial shifts in technology since the DORI classification was introduced in 2014.
Developments include improvements in camera resolutions and the capabilities of AI-driven analytics, which demand more precise detail definitions. The DORI framework's four levels—detection, observation, recognition, and identification—no longer sufficed for modern, complex monitoring needs. OODPCVS introduces seven distinct levels: overview, outline, discern, perceive, characterize, validate, and scrutinize.
Reasons for classification changes
Advances in video technology necessitated the update of classification standards
Advances in video technology necessitated the update of classification standards. Camera resolution has significantly progressed, with current systems leveraging high-resolution, multi-sensor cameras that outperform previous technical assumptions.
Sophisticated AI analytics now require higher pixel densities for tasks such as facial recognition and vehicle plate identification. Diverse industry applications further fuelled the demand for refined image detail definitions across various complex scenarios.
Comparing video monitoring standards
The former DORI framework, detailed in IEC 62676-4:2014, included four image detail classifications: detection (25 px/m), observation (62 px/m), recognition (125 px/m), and identification (250 px/m). In contrast, OODPCVS provides more granularity with levels designed to accommodate specific operational needs. For instance, identifying a person or license plate now requires 500 px/m under the "Validate" level, as opposed to 250 px/m under DORI's "Identification" level.
Benefits of OODPCVS
This alignment improves AI performance by matching each task to its corresponding operation
The implementation of the OODPCVS framework enhances operational effectiveness in video monitoring systems.
It enables precise system planning, aligning camera deployments more closely to pixel density requirements for specific monitoring zones. This alignment improves AI performance by matching each task to its corresponding operation and pixel density level, enhancing overall reliability and system performance.
Impact on security industry
The new standard promotes a unified approach across the security sector, facilitating improved collaboration among manufacturers, integrators, and end users. This common framework reduces ambiguity in system design and execution, ensuring that all stakeholders work towards aligned objectives.
Hikvision's response
In response to these changes, Hikvision is updating its product specifications and design tools to align with the OODPCVS framework.
These updates are intended to assist their partners in implementing solutions that meet contemporary standards while also supporting legacy systems. Hikvision remains dedicated to offering guidance and solutions that ensure compliance with new industry requirements, facilitating success in their partners' security endeavours.
Video monitoring standards play a key role in the development of the industry. Last year, the well-known DORI framework, which was introduced in 2014, was replaced by OODPCVS, a new, more sophisticated classification which better reflects the needs of modern technologies. In response to this shift, Hikvision is actively aligning its products and design resources to support our partners in navigating this transition to higher industry standards.
As technology advances, so the relevant industry standards that govern it need to evolve. Last year, the IEC 62676-4:2025 standard, which is published by the International Electrotechnical Commission (IEC), introduced a major update to how image detail levels are classified and evaluated in video monitoring systems. This reflects significant advances in digital imaging, AI-driven video analytics, and the growing need for more refined visual detail definitions in modern security applications.
Several key developments
Why did the image detail classification need to evolve?
Since the previous edition of the standard was released in 2014, several key developments combined to make an update essential.
Substantial advances in camera resolution - Perhaps the most significant of these has been the substantial advances in camera resolution. Today’s video systems rely on high-resolution and multi-sensor cameras. These greatly out-perform the technical assumptions made in the Detection, Observation, Recognition, and Identification (DORI) image detail classification framework that was introduced in 2014.
AI analytics requires clearer, finer image detail definitions - Since 2014, AI-driven analytics has transformed the capability of video monitoring systems. Today they detect motion, people, vehicles, and animals, are used for identifying individuals, reading vehicle license plates and analyzing activities and events. But all of these applications depend on high granularity of pixel density within the video. Moreover, each use case requires a specific level of image detail, which the older framework could not sufficiently describe.
Industry applications have grown in complexity - Since the DORI classification was introduced, the range of industries which have adopted video monitoring technology has greatly increased. Video-based applications are now widely used in transportation, energy, retail, and logistics. At the same time, the application scenarios have grown significantly in complexity. These increasingly diverse deployments demand more consistent and precise definitions of image requirements to ensure reliable performance.
Describing nuanced tasks
How does the new OODPCVS classification compare with DORI?
DORI’s simplicity made it widely adopted, but it also introduced limitations. With only four broad levels (Detection, Observation, Recognition, and Identification), it was not well-suited for describing nuanced tasks or meeting the precision required by modern video analytics. This created the need for a more flexible, granular framework.
To address these evolving needs, the 2025 standard introduces a more detailed image detail classification framework – Overview, Outline, Discern, Perceive, Characterize, Validate, and Scrutinize (OODPCVS). With these seven clearly defined levels, the OODPCVS framework offers a more modern, AI-ready evolution of the DORI framework, providing explicit guidance for determining the required camera resolution for diverse applications.
For example, the pixel density required to identify a person or read a license plate is raised from 250 px/m (classified in the DORI framework as “Identification”), to 500 px/m (classified as “Validate” in OODPCVS). This ensures sufficient image detail, especially in low-light conditions or dynamic scenes with motion blur.
Performing certain operations
DORI: Four-level image detail classification
DORI, based on IEC 62676-4:2014, defines four levels of image detail required to perform certain operations:
| Operation | Image Detail | Pixel Density |
| Detection | Determine whether a person or object is present | 25 px/m |
| Observation | Observe characteristic details or behavior patterns without identification | 62 px/m |
| Recognition | Determine a person or vehicle as the same one seen earlier | 125 px/m |
| Identification | Identify a person or read specific identifying information like a plate number | 250 px/m |
Seven categories of operations
OODPCVS: Seven-level image detail classification
The new IEC 62676-4:2025 standard introduces seven categories of operations and image detail levels:
| Operation | Image Detail | Pixel Density |
| Overview | General awareness, such as detecting a distant moving object | 20 px/m |
| Outline | Distinguish object shapes and track movement | 40 px/m |
| Discern | Distinguish between persons, vehicles, and animals | 80 px/m |
| Perceive | Detect presence and movement with more certainty, though characteristics remain unclear | 125 px/m |
| Characterize | Identify object features such as a person’s gait or a vehicle type | 250 px/m |
| Validate | Verify known persons or read license plates | 500 px/m |
| Scrutinize | Forensic-level inspection that meets passport-photo clarity standards | 1500 px/m |
Video monitoring systems
What are the practical benefits of OODPCVS?
The OODPCVS framework ensures that video monitoring systems can better support real-world operational needs, enhancing accuracy, reliability, and system design workflows.
- More accurate system planning - Designers can now define more precise pixel density targets for each monitored zone, enabling better informed camera and lens selection. This gives end users and operators confidence that the system will perform as intended.
- Better alignment with AI performance needs - Each AI task can be matched to a defined combination of operation and pixel density level. For example, “Classified Object Detection at Perimeters” could be defined as “Discern” at 80 px/m. “Vehicle Feature Analysis” could be “Characterize” at 250 px/m. “Facial Recognition” might be “Validate” at 500 px/m, and “Forensic Detail Capture” defined as “Scrutinize” at 1500 px/m.
- More reliable video evidence and daily operations - The higher clarity levels required by OODPCVS will improve reliability and efficiency of video analytics and incident investigation and will give operators better overall situation awareness.
- A unified communication framework for the industry - Manufacturers, system integrators, installers, and end users can reference the same set of classification definitions, improving collaboration and reducing ambiguity.
Updating product specifications
The evolution of IEC 62676-4 represents an important advancement for the security industry, aligning image detail classification with modern digital imaging and AI-driven use cases. As the industry moves toward the IEC 62676-4:2025 standard and the OODPCVS framework, they recognize how essential it is for their partners to work with tools and products that reflect the latest requirements.
Hikvision is now actively updating product specifications, system design tools, and other related resources. These updates will help partners plan camera placements more precisely and design projects with greater confidence.
They remain committed to providing clear guidance and future-ready solutions that support both the new standard and legacy deployments—ensuring that every project meets the performance levels and operational requirements defined by customers.
