Deep Learning Surveillance Series

IBBVision Deep Learning Surveillance Software

What is Deep Learning Surveillance?

Deep Learning Surveillance is an AI-powered video analysis system developed by IBBVision. This software uses deep neural networks and machine learning technologies to analyze video streams from security cameras in real time, without the need for human intervention. By automatically detecting objects, people, vehicles, and abnormal situations, the system transforms traditional surveillance systems into intelligent and proactive security solutions.

Working Principle

IBBVision Edge AI Vision Computer devices are delivered as standard with Core software installed. The primary functions of this Core software are:

• Receiving video streams from cameras via RTSP/ONVIF protocols

• Providing live video monitoring capabilities

• Managing the core video infrastructure

• Controlling camera connections

However, the Core software does not include any artificial intelligence analysis capabilities. To enable image processing, object detection, and anomaly detection on the device, the Deep Learning Surveillance software must be licensed and installed.

Once the Deep Learning Surveillance software is installed on the device, the system automatically begins real-time image analysis. Each camera channel is processed independently, and detected events are instantly reported to the operator.

Modular Architecture

Deep Learning Surveillance software is built on a modular architecture. This structure allows each organization to select the analysis modules that suit its needs and license only the features it will use. The modular approach also enables flexible system expansion.

Important Note: Multiple Deep Learning Surveillance analysis modules can run simultaneously on a single Edge AI Vision Computer device. For example, a single device can concurrently perform different analyses such as suspicious behavior detection, fall detection, violence detection, and zone intrusion detection. The number of event analyses that can be performed simultaneously depends entirely on the device's processing capacity. IBBVision Edge AI Vision Computer devices can analyze between 1 and 64 camera feeds simultaneously.

Analysis Modules

Deep Learning Surveillance software includes a wide variety of analysis modules. These modules are categorized, with each one developed to address a specific security or operational need.

PERSON AND OBJECT DETECTION MODULES – Selected Examples

• Human Detection: Detects all human figures within the camera's field of view and determines their positions. Accurately distinguishes humans from other objects.

• Vehicle Detection: Detects and classifies cars, trucks, motorcycles, buses, and other vehicle types.

• Object Detection: Detects specific objects such as bags, backpacks, suitcases, weapons, knives, helmets, vests, and fire extinguishers.

• Face Detection and Recognition: Detects faces in camera images, matches them against a database, and performs person identity verification.

• License Plate Recognition (ANPR/LPR): Automatically reads, recognizes, and records vehicle license plates. Supports various country-specific plate formats.

• Vehicle-Pedestrian Differentiation: Distinguishes vehicles from pedestrians in traffic flow and analyzes the movements of both groups separately.

• Animal Detection: Detects animals such as cats, dogs, birds, horses, and cows, and differentiates them from people.

• Color and Feature Detection: Detects vehicles of a specific color or people wearing specific clothing. Can filter, for example, for red cars or people wearing blue jackets.

• Multi-Target Tracking: Enables simultaneous tracking of multiple objects at once.

• Person-Based Tracking: Tracks a specific designated individual across multiple cameras and maps their movement route.

BEHAVIOR AND ANOMALY ANALYSIS MODULES – Selected Examples

• Suspicious Behavior Detection: Detects suspicious activities such as loitering, covert approaches, continuous wandering in the same area, and hiding, which may indicate theft preparation.

• Anomaly Detection: Identifies unexpected or unusual situations. Analyzes any movement that deviates from normal patterns.

• Fall Detection: Automatically detects when a person suddenly falls to the ground. Particularly critical for elderly care facilities, hospitals, and workplaces.

• Violence Detection: Detects physical altercations including fighting, pushing, punching, and kicking.

• Running and Fleeing Detection: Detects individuals moving significantly faster than normal walking speed. Provides early warning of emergencies, panic, or escape situations.

• Risky Movement Detection: Detects risky behaviors such as sudden movements, abrupt changes in direction, and erratic motions.

• Grouping Detection: Detects groups of people gathering in a specific area. Provides early warning of uncontrolled crowd formation.

• Proximity Detection: Detects social distancing violations. Identifies when people get closer to each other than the defined distance.

• Tunnel Vision Detection: Detects sabotage attempts such as blocking the camera's view or covering the lens.

• Behavior Pattern Learning: Learns normal behavior patterns in specific areas over time and detects deviations from these patterns.

SECURITY AND ENVIRONMENTAL ANALYSIS MODULES – Selected Examples

• Zone Intrusion Detection: Detects unauthorized entry into or exit from designated restricted areas. Identifies security perimeter breaches.

• Unauthorized Area Access: Detects unauthorized access to specially defined sensitive areas (e.g., management offices, server rooms, production lines).

• Abandoned Object Detection: Detects suspicious objects such as bags, suitcases, and packages that remain stationary for a defined period. Provides early warning against terrorist and sabotage risks.

• Stolen Object Detection: Detects the removal of a specific object from its designated location. Used for theft prevention in museums, stores, and warehouses.

• Wrong Direction Movement: Detects vehicles or people moving in the wrong direction in areas defined as one-way.

• Fire and Smoke Detection: Detects early signs of flames, fire, smoke, and soot from camera feeds.

• Boundary Intrusion Detection: Detects the crossing of a defined line or boundary. Used in areas such as security lines, parking space limits, and platform edges.

• Timeout Detection: Detects when a person or object remains in a designated area longer than the permitted duration.

• Environmental Change Detection: Detects environmental changes in the camera's field of view (e.g., flooding, landslides, post-fire changes, etc.).

• Drone and UAV Detection: Detects unmanned aerial vehicles and drones entering the airspace.

DENSITY AND OPERATIONAL ANALYSIS MODULES – Selected Examples

• Density Analysis: Measures the density of people or vehicles in a specific area. Calculates the number of individuals in the area in real time.

• Crowd Management: Detects overcrowded areas and issues alerts in the event of capacity exceedance. Critical for venues such as concerts, stadiums, and shopping malls.

• Personal Protective Equipment (PPE) Compliance Check: Automatically monitors the use of mandatory safety equipment at work sites, including helmets, safety harnesses, protective vests, goggles, gloves, and hard hats.

• Motion Analysis: Detects abnormal or unexpected movement patterns. Analyzes movement direction, speed, and shape.

• Entry-Exit Counting: Automatically calculates the number of people or vehicles entering and exiting a specific point. Used for customer traffic analysis and personnel tracking.

• Parking Occupancy Detection: Detects empty and occupied parking spaces in parking lots and calculates the occupancy rate.

• Dwell Time Analysis: Measures the time customers or personnel spend at specific points. Used for queue management and service optimization.

• Visitor Frequency Analysis: Calculates how many distinct individuals visit a specific area within a defined time interval.

• Thermal Anomaly Detection: Works with thermal cameras to detect individuals with elevated body temperature. Used for fever screening and pandemic prevention measures.

• Production Line Monitoring: Tracks products on the production line, analyzing defective products, stoppages, and production speed.

SPECIALIZED AND SECTOR-SPECIFIC ANALYSIS MODULES – Selected Examples

• Retail Theft Prevention: Detects suspicious movements in stores, such as product concealment and escape attempts. Integrates with customer behavior analysis.

• Customer Behavior Analysis: Analyzes customer movements in retail spaces, time spent in aisles, and products of interest.

• Driving Behavior Analysis: Detects vehicle speed, lane violations, sudden braking, and acceleration patterns. Used for fleet management and safe driving monitoring.

• Cargo and Freight Tracking: Tracks product movements, shipping, and delivery processes in warehouses and logistics centers.

• Patient and Elderly Monitoring: Detects patient movements, fall risks, and bed exit situations in hospitals and nursing homes.

• Student Monitoring: Detects student movements in schools, unauthorized area access, and movements outside class hours.

• Maritime and Coastal Security: Detects objects on the water surface, boat movements, and suspicious vessels approaching the coast.

• Agriculture and Livestock Monitoring: Detects animal movements, crop conditions, and unauthorized entries in fields, greenhouses, and farms.

• Face Mask Detection: Detects whether individuals are wearing face masks.

• Audio and Noise Analysis: Detects acoustic anomalies such as increased noise levels, shouting, glass breaking, and alarm sounds.

Core Advantages

• Low False Alarm Rate: Deep Learning Surveillance can distinguish between ordinary elements such as wind, light changes, moving shadows, or animals and genuine threats. This minimizes unnecessary alerts and allows security teams to focus only on real risks. The false alarm problems common to traditional systems are largely eliminated.

• Proactive Security: While conventional surveillance systems only record footage, Deep Learning Surveillance instantly detects risky moments and immediately alerts security teams. Enables intervention at the time of the incident. Instead of reviewing recordings after an event occurs, the system issues an alarm while the event is happening.

• Automation and Efficiency: It is practically impossible for human operators to continuously monitor dozens of camera feeds. Deep Learning Surveillance automatically handles this workload, allowing security personnel to focus solely on real incidents. This significantly increases workforce efficiency.

• Uninterrupted 24/7 Operation: All analysis processes run continuously, without requiring operator intervention and independently of an internet connection. The system resumes operation even after power outages. It is not affected by fatigue, distraction, or shift changes.

• Data Privacy and Security: All image processing is performed locally (edge computing). Sensitive image data is analyzed securely without being transmitted outside the facility. This feature is especially critical for institutions with strict privacy requirements. Camera footage is not continuously sent to an external cloud platform.

• Scalable Modular Architecture: Modules can be licensed individually based on different analysis needs. New analytical capabilities can be added to the system as needs change or grow. Organizations pay only for the features they will use.

• Time and Cost Savings: The need to review hours of video footage after an incident is eliminated. All events are detected and flagged instantly. Financial losses from theft, sabotage, and security breaches are minimized through prevention.

Use Cases

Retail and Store Management

• Theft and customer behavior analysis

• In-store security optimization

• Personnel tracking and productivity analysis

• Shelf product status monitoring

• Customer heatmap creation

Urban and Traffic Safety

• License plate recognition and vehicle tracking

• Traffic density and flow analysis

• Rule violation detection (wrong way, red light, speed, etc.)

• Pedestrian safety and intersection analysis

• Parking occupancy tracking

Critical Facilities and Public Areas

• Unauthorized passage and suspicious object detection at airports

• Suspicious behavior and object detection at stations

• Occupational safety inspection at factory sites

• Hazardous area supervision at energy facilities

• Port and terminal security

Workplaces and Corporate Buildings

• Personnel tracking and security

• Unauthorized area access control

• Fire and emergency early warning system

• Visitor management

• Parking lot and vehicle entry-exit control

Healthcare Institutions

• Patient and elderly monitoring, fall detection

• Personnel safety

• Unauthorized area access control

• Emergency and violence incident detection

• Patient visitor management

Educational Institutions

• Student monitoring and safety

• Campus access control

• Detection of in-school violence incidents

• Fire and emergency alerts

• Unauthorized person detection

Hotels and Tourism

• Customer experience analysis

• Personnel and service tracking

• Security and unauthorized area supervision

• Parking and vehicle management

Warehouses and Logistics

• Personnel and vehicle tracking

• Occupational safety inspection

• Product movement and inventory tracking

• Theft prevention

• Forklift and equipment safety

Manufacturing and Industry

• Production line monitoring and quality control

• Safety equipment compliance inspection

• Personnel movements and productivity analysis

• Machine and equipment safety monitoring

Sports Venues and Event Areas

• Crowd density and management

• Security risk detection

• Unauthorized area entry

• Emergency and panic detection

Licensing Information

Deep Learning Surveillance software is licensed independently of the Edge AI Vision Computer hardware:

Each analysis module can be licensed separately and added to the system as needed. Organizations can achieve a cost-effective solution by selecting only the modules that align with their security and operational needs.

Frequently Asked Questions

Q: Is Deep Learning Surveillance software included with the Edge AI Vision Computer out of the box?
A: No. Edge AI Vision Computer devices are delivered with Core software as standard. The Deep Learning Surveillance software and its analysis modules are licensed separately and installed on the device subsequently.

Q: How many different analyses can run simultaneously on a single device?
A: Multiple Deep Learning Surveillance analysis modules can run simultaneously on a single device. The number of event analyses that can be performed concurrently depends entirely on the device's capacity. IBBVision Edge AI Vision Computer devices can analyze between 1 and 64 camera feeds simultaneously.

Q: Can a new analysis module be added later?
A: Yes. Thanks to the modular structure, new analytical capabilities can be easily added to the system with proper licensing.

Q: Can Deep Learning Surveillance operate without an internet connection?
A: Yes. The system's core local functions and licensed AI analyses running on the device can operate 24/7 without requiring a continuous internet connection.

Q: Which camera brands are compatible?
A: Deep Learning Surveillance is compatible with all IP camera brands that can stream video via RTSP and ONVIF protocols. It is independent of camera manufacturer.

Q: Can Deep Learning Surveillance integrate with third-party software?
A: The system provides integration with third-party Video Management Systems and Central Management Systems via REST-based service outputs and webhook support.

Conclusion

IBBVision Deep Learning Surveillance is a comprehensive AI-powered video analysis platform that transforms traditional surveillance systems into intelligent, proactive, and autonomous security solutions. Thanks to its modular architecture, it offers analysis capabilities suitable for organizations of all sizes. Working in conjunction with Edge AI Vision Computer devices, this software processes image data locally, ensuring both high performance and data privacy.

Equipped with artificial intelligence and deep learning technologies, this system instantly detects anomalies that the human eye might miss, minimizes false alarm rates, and provides security teams with real-time intervention capabilities. It is a strategic solution for facilities, institutions, and businesses, delivering not only security but also operational efficiency and business intelligence.

All Deep Learning Surveillance software is developed exclusively by IBBVision. Third-party software is not supported. The scope of hardware, software, and AI analysis features may vary depending on product configuration and the relevant licenses. All rights reserved.