Artificial Intelligence has rapidly transformed the video surveillance industry. Modern security cameras are no longer just recording devices; they are intelligent systems capable of analyzing scenes, detecting objects, and identifying behaviors in real time.
AI-powered video analytics allows surveillance systems to automatically recognize and analyze objects within video streams, convert them into structured or semi-structured data, and generate actionable insights.
These intelligent capabilities can be implemented in several ways:
- Edge computing (on-camera AI processing)
- Backend VMS or server processing
- Cloud-based video analytics
- Hybrid cloud-edge architectures
In this article, we summarize the main categories of intelligent video analytics in security cameras and the key performance requirements used to evaluate them.
1. Types of Video Analytics in Surveillance Systems
AI video analytics in CCTV systems can generally be divided into three major categories.
1.1 Face and Human Analysis
Face and person analysis focuses on identifying and understanding human targets in surveillance footage.
Typical capabilities include:
- Face detection
- Face recognition
- Face attribute analysis
- Person detection
- Person attribute recognition
These technologies are widely used in:
- Access control systems
- Smart city security
- Public safety monitoring
- Retail analytics
For example, a camera can detect a person entering a restricted area and determine attributes such as clothing color or whether the person is carrying a bag.
1.2 Vehicle Analysis
Vehicle analytics refers to the automatic detection and identification of vehicles and traffic conditions.
Key functions include:
- Vehicle detection
- License plate recognition (LPR/ANPR)
- Vehicle attribute recognition
- Traffic flow analysis
- Non-motor vehicle detection
With the rapid growth of urban transportation, electric scooters, bicycles, and delivery vehicles are becoming increasingly important targets for intelligent detection.
Vehicle analytics is widely used in:
- Smart traffic systems
- Parking management
- Highway monitoring
- City surveillance networks
1.3 Event and Behavior Analysis
Event analytics detects abnormal behaviors or specific events occurring in the monitored scene.
Typical functions include:
- Intrusion detection
- Tripwire crossing detection
- Loitering detection
- Object left behind detection
- Object removal detection
- Abnormal behavior detection
These features are essential for perimeter protection and proactive security monitoring.
2. Basic Image Quality Requirements for Video Analytics
AI analytics performance depends heavily on image quality. Surveillance cameras must meet several basic requirements to ensure reliable analysis:
- Adequate image brightness
- Low image blur
- Defog capability
- Target area enhancement
- Lens obstruction detection
- Video jitter detection
- Scene change detection
- Accurate color reproduction
Poor image quality can significantly reduce AI recognition accuracy.
3. Face and Person Analytics Performance Requirements
3.1 Face Detection
When the distance between pupils is at least 30 pixels, the system should output:
- number of detected faces
- face size (width and height in pixels)
- face location
Performance requirements:
- Face detection rate ≥ 95%
- Detection accuracy ≥ 99%
For crowded scenes:
- At least 10 faces per frame
- Advanced systems support 30 faces or more
3.2 Face Attribute Recognition
Cameras should be able to identify facial attributes such as:
- Gender
- Wearing accessories (glasses, hat)
- Hair style (long hair, short hair, bald)
- Age group
Average recognition accuracy should be ≥ 85%.
3.3 Face Recognition
Face recognition systems compare detected faces against a database.
Typical database sizes include:
- 1,000 faces
- 10,000 faces
- 100,000 faces
Performance requirements:
- False alarm rate ≤ 5%
- Miss detection rate ≤ 5%
3.4 Person Detection
The system should detect human targets with a minimum size of 64 × 128 pixels.
Output information includes:
- number of persons
- bounding box size
- location coordinates
Performance requirements:
- Detection rate ≥ 95%
- Accuracy ≥ 99%
Advanced systems can detect 30 people in a single frame.
3.5 Person Attribute Recognition
Person attribute analytics can identify:
Clothing Attributes
- Black
- White
- Gray
- Red
- Green
- Blue
- Yellow
- Orange
- Purple
- Pink
- Brown
Movement Information
- Motion direction (front, back, side)
- Movement state (walking, riding)
Advanced Attributes
- Carrying objects (backpack, handbag, suitcase, umbrella)
- Accessories (hat, helmet)
- Clothing patterns (solid, stripes, plaid)
Average recognition accuracy should be ≥ 85%.
4. Vehicle Analytics Capabilities
4.1 Vehicle Detection
The system should detect vehicles when license plate width exceeds 80 pixels.
Output includes:
- vehicle count
- vehicle size
- vehicle position
Performance requirements:
- Detection rate ≥ 95%
- Accuracy ≥ 99%
4.2 License Plate Recognition (LPR)
The system should recognize:
- License plate location
- Plate number
- Plate color
- Plate type
Accuracy requirements:
- Daytime recognition accuracy ≥ 95%
- Nighttime recognition accuracy ≥ 90%
Advanced systems can detect:
- Plate occlusion
- Plate tampering
- Plate layer abnormalities
4.3 Vehicle Attribute Recognition
Cameras can identify vehicle attributes such as:
- Vehicle type
- Vehicle color
- Vehicle brand
Average recognition accuracy should be ≥ 80%.
4.4 Driver Behavior Detection
Advanced vehicle analytics can detect:
- Driver not wearing a seatbelt
- Driver using a mobile phone
- Passengers holding a child
- Objects inside the vehicle
4.5 Traffic Flow Statistics
For vehicle targets larger than 120 × 120 pixels, systems can analyze:
- Vehicle count by lane
- Time-based traffic statistics
- Vehicle type statistics
Traffic flow analysis accuracy should be ≥ 90%.
4.6 Non-Motor Vehicle Detection
For non-motor vehicles with width ≥ 80 pixels:
- Detection rate ≥ 90%
- Detection accuracy ≥ 90%
Supported vehicle types include:
- bicycles
- electric scooters
- tricycles
5. Event Detection and Behavioral Analytics
5.1 Intrusion Detection
Detects events such as:
- Tripwire crossing
- Area intrusion
- Loitering
Requirements:
- Target size ≥ 32 × 32 pixels
- Detection rate ≥ 90%
- False alarm rate ≤ 5%
5.2 Object Left / Removed Detection
Detects objects left behind or removed from the scene.
Requirements:
- Target size ≥ 32 × 32 pixels
- Detection rate ≥ 90%
- False alarm rate ≤ 5%
5.3 Abnormal Behavior Detection
For person targets larger than 64 × 128 pixels, abnormal behavior detection can include:
- Running
- Falling
- Climbing
- Wrong-direction movement
5.4 Crowd Density Detection
Crowd detection is triggered when the number of people exceeds a preset threshold.
Requirements:
- Shoulder width ≥ 32 pixels
- Detection accuracy ≥ 90%
This feature is widely used in:
- stadiums
- transportation hubs
- public events
- shopping malls
Conclusion
AI video analytics is rapidly becoming a standard feature in modern surveillance systems. By combining advanced algorithms with high-quality imaging, security cameras can automatically detect people, vehicles, and events, significantly improving security efficiency.
With the development of edge AI chips and deep learning algorithms, intelligent surveillance systems will continue to evolve toward real-time analytics, higher accuracy, and broader applications.