IBBVision DLN 24 — AI-Powered Enterprise NVR and Video Analytics Platform

IBBVision DLN 24 has been developed as an artificial intelligence–based video analytics platform that goes beyond traditional NVR systems limited to video recording. The platform actively processes and analyzes video streams received from the field, transforming visual data into meaningful operational intelligence. While simultaneously interpreting live camera feeds, the system also enables AI-assisted querying of historical recordings without the need for manual playback, significantly reducing post-incident investigation time and minimizing operator dependency.

IBBVision DLN 24 provides a scalable, stable, and enterprise-grade analytics infrastructure designed for corporate facilities, municipal areas, campus environments, industrial sites, and high-security zones.

24-Channel IP Camera Support and Parallel Processing Architecture

The platform processes and records video streams from up to 24 IP cameras concurrently. Each camera is assigned an independent analysis queue with dedicated processing resources. The parallel inference architecture dynamically evaluates scene density to optimize frame processing frequency, preventing latency under high load conditions. As a result, analytical continuity is maintained, alarms are generated in real time, and uninterrupted recording is ensured.

Real-Time AI-Based Video Analytics

Each camera operates with an independent analytics engine supporting enterprise-level scenarios such as object detection, human and vehicle recognition, multi-target tracking, individual-based tracking, intrusion detection, crowd density analysis, and motion behavior analysis. The system can automatically switch between performance-optimized and resource-efficient operating modes based on scene complexity and system resource utilization.

AI Analysis on Historical Recordings (Retrospective AI)

IBBVision DLN 24 is not limited to live stream analytics; it also enables AI-based target search on recorded video data. Queries such as where a specific individual appeared, which cameras a particular vehicle passed through, or during which time intervals a face was detected can be executed retrospectively. These operations rely on feature vectors generated during recording, accelerating post-event investigations and significantly reducing the need for manual review.

Multi-Layer Security and Behavior Analytics

The multi-layer analytics framework supports security-focused use cases including suspicious object detection, abnormal behavior recognition, unauthorized area access, personal protective equipment compliance, reverse-direction movement detection, vehicle-pedestrian classification, and density analysis. The alarm engine evaluates events by time, zone, and camera, generating a prioritized and manageable alarm workflow.

Custom Model Development and Institutional Integration

The DLN 24 platform allows institutions to integrate up to 20 custom AI models tailored to specific operational requirements. The model lifecycle includes data collection, label validation, performance evaluation, and version-controlled deployment. This structure enables secure and controlled integration of sector-specific risk scenarios into the system.

ROI (Region of Interest)-Based Prioritization

Regions of interest can be defined on a per-camera basis. Analytical intensity can be increased in critical areas while reducing processing load in non-essential zones. This approach balances system resources and helps lower false alarm rates.

Alarm and Incident Management Layer

Alarms generated by the system are supported by visual overlays, real-time operator notifications, event logging, rapid rewind functionality, and direct access to relevant video segments. Alarm histories can be reported and integrated into task-oriented operational workflows.

Reporting and Analytics Monitoring Dashboard

The platform generates measurable, decision-support data for security and operations teams. Camera-level analytics loads, alarm distributions, model performance metrics, and risk density maps can be monitored and leveraged for operational planning and optimization.

Technical Architecture and Processing Pipeline

The system architecture is built around RTSP stream ingestion, time-stamped frame processing, a parallel inference pipeline, object and behavior analysis, embedding generation, and simultaneous alarm engine execution. Camera-level resource allocation and adaptive frame processing ensure stable performance during long-term and high-intensity operations.

Integration and System Compatibility

IBBVision DLN 24 offers RTSP compatibility independent of IP camera brands. It can integrate with third-party VMS and CMS solutions and supports REST-based service outputs as well as webhook-based alarm integrations.

Key Application Areas

Corporate facilities, municipal and public spaces, manufacturing and quality control lines, commercial enterprises, energy sites, and critical infrastructure zones represent the primary application domains targeted by the DLN 24 platform.

IBBVision DLN 32 — AI-Powered Enterprise NVR and Video Analytics Platform

IBBVision DLN 32 is designed as an artificial intelligence–based video analytics platform that goes beyond conventional NVR systems limited to video recording. The platform processes, interprets, and contextualizes video streams received from the field, transforming them into actionable data for operational workflows. While simultaneously analyzing live camera feeds, the system enables AI-assisted querying of historical recordings without the need for manual playback, significantly shortening post-incident evaluation times and substantially reducing operator dependency.

IBBVision DLN 32 delivers a scalable, stable, and enterprise-grade analytics infrastructure for corporate facilities, municipal areas, production sites, campus environments, and locations with elevated security requirements.

32-Channel IP Camera Support and Parallel Processing Architecture

The platform processes and records video streams from up to 32 IP cameras concurrently. An independent analysis queue is defined for each camera, and processing resources are allocated on a per-camera basis. The parallel inference architecture dynamically evaluates scene density to optimize frame processing frequency and minimize latency. This ensures uninterrupted analytics continuity, timely alarm generation, and seamless recording workflows.

Real-Time AI-Based Video Analytics

Each camera operates with an independently running analytics engine supporting enterprise use cases such as object detection, human and vehicle recognition, multi-target tracking, individual-based tracking, intrusion detection, crowd density analysis, and motion behavior analysis. The system can automatically switch between performance-focused and resource-efficient operating modes based on scene complexity and resource utilization.

AI Analysis on Historical Recordings (Retrospective AI)

IBBVision DLN 32 is not limited to live stream analytics; it also enables AI-based target search on recorded video content. Retrospective queries can determine where a specific individual appeared, which cameras a particular vehicle passed through, or during which time intervals a face was detected. These operations are performed using feature vectors generated during recording, accelerating post-event investigations and reducing the need for manual video review.

Multi-Layer Security and Behavior Analytics

The multi-layer analytics infrastructure supports security-focused scenarios including suspicious object detection, abnormal behavior recognition, unauthorized area access, personal protective equipment monitoring, reverse-direction movement detection, vehicle–pedestrian classification, and density analysis. The alarm engine evaluates events based on time, zone, and camera, generating a prioritized and manageable alarm flow.

Custom Model Development and Institutional Integration

The DLN 32 platform allows the integration of up to 20 custom AI models based on institutional operational requirements. The model lifecycle encompasses data collection, label validation, performance evaluation, and version-controlled deployment. This enables sector-specific risk scenarios to be securely and systematically integrated into the platform.

ROI (Region of Interest)-Based Prioritization

Regions of interest can be defined for each camera. Analytical intensity is increased in critical areas while processing load is reduced in non-essential zones. This approach balances resource utilization and helps reduce false alarm rates.

Alarm and Incident Management Layer

Alarms generated by the system are supported by visual overlays, real-time operator notifications, event logging, rapid rewind functionality, and direct access to relevant moments. Alarm histories can be reported and integrated into task-oriented operational workflows.

Reporting and Analytics Monitoring Dashboard

The platform generates measurable, decision-support data for security and operations teams. Camera-level analytics loads, alarm distributions, model performance metrics, and risk density maps can be monitored and utilized in operational planning processes.

Technical Architecture and Processing Pipeline

The system architecture is built around RTSP stream ingestion, time-stamped frame processing, a parallel inference pipeline, object and behavior analysis, embedding generation, and concurrent alarm engine execution. Camera-level resource allocation and adaptive frame processing ensure stable performance during long-duration and high-intensity operations.

Integration and System Compatibility

IBBVision DLN 32 provides RTSP compatibility independent of IP camera brands. It can integrate with third-party VMS and CMS solutions and supports REST-based service outputs as well as webhook-based alarm integrations.

Key Application Areas

Corporate campuses, municipal and public areas, production and quality control sites, commercial facilities, energy zones, and critical infrastructure environments represent the primary application domains targeted by the DLN 32 platform.

IBBVision DLN 40 — AI-Powered Enterprise NVR and Video Analytics Platform

IBBVision DLN 40 has been developed as an artificial intelligence–based analytics platform that goes beyond traditional NVR solutions, evolving from a system focused solely on recording into one that processes incoming video streams, derives meaning from visual data, and delivers actionable insights to operational workflows. While simultaneously interpreting live camera feeds, the system enables AI-assisted querying without the need to revisit historical recordings, thereby shortening post-incident analysis times and significantly reducing operator dependency.

IBBVision DLN 40 provides a scalable, stable, and enterprise-grade analytics infrastructure for corporate campuses, municipal areas, production sites, campuses, and high-security environments.

40-Channel IP Camera Support and Parallel Processing Architecture

The platform processes and records video streams from up to 40 IP cameras concurrently. An independent analysis queue is defined for each camera, with processing resources allocated on a per-camera basis. The parallel inference architecture dynamically evaluates scene density to optimize frame processing frequency and minimize latency. As a result, analytical workflows remain uninterrupted, alarms are generated in a timely manner, and recording continuity is preserved.

Real-Time AI-Based Video Analytics

Each camera runs an independent analytics engine supporting enterprise use cases such as object detection, human and vehicle recognition, multi-target tracking, individual-based tracking, intrusion detection, crowd density analysis, and motion behavior analysis. The system can automatically switch between performance-focused and resource-efficient modes based on scene complexity and resource utilization.

AI Analysis on Historical Recordings (Retrospective AI)

IBBVision DLN 40 is not limited to live-stream analytics; it also enables AI-based target search across recorded video data. Retrospective queries can identify where a specific individual appeared, which cameras a particular vehicle passed through, or during which time intervals a face was detected. These queries are executed using feature vectors generated during recording, accelerating post-incident investigations and reducing the need for manual review.

Multi-Layer Security and Behavior Analytics

The multi-layer analytics framework supports security-focused scenarios including suspicious object detection, abnormal behavior recognition, unauthorized area access, personal protective equipment monitoring, reverse-direction movement detection, vehicle–pedestrian classification, and density analysis. The alarm engine evaluates events by time, zone, and camera to generate a prioritized and manageable alarm workflow.

Custom Model Development and Institutional Integration

The DLN 40 platform allows the integration of up to 20 custom AI models tailored to institutional operational requirements. The model lifecycle encompasses data collection, label validation, performance evaluation, and version-controlled deployment, enabling secure and controlled integration of sector-specific risk scenarios.

ROI (Region of Interest)-Based Prioritization

Regions of interest can be defined for each camera. Analytical intensity is increased in critical areas while processing load is reduced in non-essential zones. This approach balances resource utilization and helps reduce false alarm rates.

Alarm and Incident Management Layer

Alarms generated by the system are supported by visual overlays, real-time operator notifications, event logging, rapid rewind functionality, and direct access to relevant moments. Alarm histories can be reported and integrated into task-oriented operational workflows.

Reporting and Analytics Monitoring Dashboard

The platform produces measurable, decision-support data for security and operations teams. Camera-level analytics loads, alarm distributions, model performance metrics, and risk density maps can be monitored and utilized in operational planning processes.

Technical Architecture and Processing Pipeline

The system architecture is built around RTSP stream ingestion, time-stamped frame processing, a parallel inference pipeline, object and behavior analysis, embedding generation, and concurrent alarm engine execution. Camera-level resource allocation and adaptive frame processing ensure stable performance during long-duration and high-intensity operations.

Integration and System Compatibility

IBBVision DLN 40 provides RTSP compatibility independent of IP camera brands. It can integrate with third-party VMS and CMS solutions and supports REST-based service outputs as well as webhook-based alarm integrations.

Key Application Areas

Corporate facilities, municipal and public spaces, production and quality control sites, shopping malls, energy zones, and critical infrastructure environments represent the primary application domains targeted by the DLN 40 platform.

IBBVision DLN 48 — AI-Powered Enterprise NVR and Video Analytics Platform

IBBVision DLN 48 is designed as an AI-enabled video analytics platform that goes beyond traditional NVR systems limited to video recording. The platform processes and analyzes video streams captured from the field, transforming visual data into meaningful insights for operational workflows. While simultaneously interpreting live camera feeds, the system also enables AI-based querying of historical recordings without the need for manual playback, thereby shortening post-incident analysis times and significantly reducing operator dependency.

IBBVision DLN 48 delivers a scalable, resilient, and enterprise-grade analytics infrastructure for corporate campuses, municipal sites, production areas, large facility complexes, and high-security environments.

48-Channel IP Camera Support and Parallel Processing Architecture

The platform processes and records video streams from up to 48 IP cameras concurrently. Each camera is assigned an independent analysis queue, and processing resources are allocated on a per-camera basis. The parallel inference architecture dynamically evaluates scene density to optimize frame processing frequency and prevent latency. As a result, analytics workflows remain uninterrupted, alarms are generated in a timely manner, and recording continuity is preserved.

Real-Time AI-Based Video Analytics

Each camera operates with an independently running analytics engine supporting enterprise use cases such as object detection, human and vehicle recognition, multi-target tracking, individual-based tracking, intrusion detection, crowd density analysis, and motion behavior analysis. The system can automatically switch between performance-focused and resource-efficient operating modes based on scene complexity and current resource utilization.

AI Analysis on Historical Recordings (Retrospective AI)

IBBVision DLN 48 is not limited to live video stream analytics; it also enables AI-based target search across recorded video content. Retrospective queries can determine where a specific individual appeared, which cameras a particular vehicle passed through, or during which time intervals a face was detected. These operations are executed using feature vectors generated during recording, accelerating post-incident investigations and minimizing the need for manual review.

Multi-Layer Security and Behavior Analytics

The multi-layer analytics infrastructure supports security-focused capabilities including suspicious object detection, abnormal behavior recognition, unauthorized area access, personal protective equipment compliance, reverse-direction movement detection, vehicle–pedestrian classification, and density analysis. The alarm engine evaluates events based on time, zone, and camera, generating a prioritized and manageable alarm workflow.

Custom Model Development and Institutional Integration

The DLN 48 platform allows the integration of up to 20 custom AI models in accordance with institutional operational requirements. The model lifecycle consists of data collection, label validation, performance evaluation, and version-controlled deployment. This structure enables secure and controlled integration of sector-specific risk scenarios into the system.

ROI (Region of Interest)-Based Prioritization

Regions of interest can be defined for each camera. Analytical intensity is increased in critical areas while processing load is reduced in non-essential zones. This approach balances resource utilization and helps reduce false alarm rates.

Alarm and Incident Management Layer

Alarms generated by the system are supported by visual overlays, real-time operator notifications, event logging, rapid rewind functionality, and direct access to relevant moments. Alarm histories can be reported and integrated into task-based operational workflows.

Reporting and Analytics Monitoring Dashboard

The platform generates measurable, decision-support data for security and operations teams. Camera-level analytics loads, alarm distributions, model performance metrics, and risk density maps can be monitored and leveraged in operational planning processes.

Technical Architecture and Processing Pipeline

The system architecture is built around RTSP stream ingestion, time-stamped frame processing, a parallel inference pipeline, object and behavior analysis, embedding generation, and concurrent alarm engine execution. Camera-level resource allocation and adaptive frame processing ensure stable performance during long-duration and high-intensity operations.

Integration and System Compatibility

IBBVision DLN 48 provides RTSP compatibility independent of IP camera brands. It can integrate with third-party VMS and CMS solutions and supports REST-based service outputs as well as webhook-based alarm integrations.

Key Application Areas

Corporate facilities, municipal and public spaces, production and quality control lines, shopping malls, energy sites, and critical infrastructure zones represent the primary application domains targeted by the DLN 48 platform.

IBBVision DLN 56 — AI-Powered Enterprise NVR and Video Analytics Platform

IBBVision DLN 56 has been developed as an AI-enabled analytics platform that goes beyond traditional NVR systems limited to video recording. It processes, interprets, and contextualizes video streams captured from the field, transforming them into actionable data for operational workflows. While simultaneously analyzing live camera feeds, the system enables AI-based querying of historical recordings without the need for repeated scanning, thereby shortening post-incident analysis times and reducing operator dependency.

IBBVision DLN 56 delivers a scalable, high-capacity, and enterprise-grade, security-focused infrastructure for corporate campuses, municipal areas, manufacturing facilities, campus environments, and critical security zones.

56-Channel IP Camera Support and Parallel Processing Architecture

The platform analyzes and records video streams from up to 56 IP cameras concurrently. Each camera is assigned an independent processing queue, with computing resources allocated on a per-camera basis. The parallel inference engine dynamically evaluates scene density to optimize frame processing rates and prevent latency. As a result, analytics continuity is maintained and alarm generation remains uninterrupted.

Real-Time AI-Based Video Analytics

Each camera operates with an independently running analytics engine that delivers advanced enterprise capabilities such as object detection, human and vehicle recognition, multi-target tracking, individual-based tracking, critical area intrusion detection, crowd density analysis, and motion behavior analysis. The system can automatically switch between performance-optimized and resource-efficient operating modes based on resource availability and scene complexity.

AI Analysis on Historical Recordings (Retrospective AI)

IBBVision DLN 56 is not limited to live-stream analytics; it also enables AI-assisted target search across recorded video content. Retrospective queries can identify where a specific individual appeared, which cameras a particular vehicle passed through, or during which time intervals a face was detected. These operations are executed using feature vectors generated during recording, accelerating post-incident review and minimizing the need for manual inspection.

Multi-Layer Security and Behavior Analytics

The multi-layer analytics framework supports critical security use cases including suspicious object detection, abnormal behavior recognition, unauthorized area access, personal protective equipment compliance monitoring, reverse-direction movement detection, vehicle–pedestrian classification, and density analysis. The alarm engine evaluates events by time, zone, and camera to generate a prioritized alarm workflow.

Custom Model Development and Institutional Integration

The DLN 56 platform allows the integration of up to 20 custom AI models based on institutional field requirements. The model lifecycle encompasses data collection, label validation, performance evaluation, and version-controlled deployment, enabling secure integration of sector-specific risk scenarios.

ROI (Region of Interest)-Based Prioritization

Regions of interest can be defined for each camera. Analytical intensity is increased in critical focal areas while processing load is reduced in non-essential regions. This approach reduces false alarm probability and ensures more balanced resource utilization.

Alarm and Incident Management Layer

Alarms generated by the system are supported by visual overlays, real-time operator notifications, event logging, rapid rewind functionality, and direct access to relevant moments. Alarm histories can be reported and integrated into task-based management workflows.

Reporting and Analytics Monitoring Dashboard

The platform produces measurable data for security and operations teams. Camera-level analytics loads, alarm distributions, model performance metrics, and risk density maps can be monitored and used within decision-support processes.

Technical Architecture and Processing Pipeline

The system architecture is built around RTSP stream ingestion, time-stamped frame processing, a parallel inference pipeline, object and behavior analysis, embedding generation, and concurrent alarm engine execution. Camera-level resource allocation and adaptive frame processing ensure stable performance during long-term operations.

Integration and System Compatibility

IBBVision DLN 56 is RTSP-compatible with all IP camera brands. It can integrate with third-party VMS and CMS solutions and supports REST-based services as well as webhook-based alarm outputs.

Key Application Areas

Corporate campuses, municipal and public areas, production lines, shopping malls, energy sites, and critical infrastructure zones represent the primary application scenarios targeted by the DLN 56 platform.

IBBVision DLN 64 — AI-Powered Enterprise NVR and Video Analytics Platform

IBBVision DLN 64 is designed as an AI-based video analytics platform that goes beyond conventional NVR systems, transforming from a device that merely records video into a system that processes visual data captured from the field, analyzes it, and converts it into operational value. While analyzing live camera streams in real time, the system also examines historical video recordings using AI-based scanning techniques without the need for manual playback, accelerating post-incident analysis processes and reducing human dependency.

IBBVision DLN 64 provides a high-capacity, scalable, and security-focused analytics infrastructure for corporate facilities, public spaces, production sites, large-scale campuses, and critical infrastructure environments.

64-Channel IP Camera Support and Parallel Processing Architecture

The system processes and stores streams from up to 64 IP cameras simultaneously. Each camera is assigned an independent analysis queue, and CPU/GPU resources are allocated on a per-camera basis. Thanks to the parallel AI inference infrastructure, adaptive frame processing is applied in high-density scenes, minimizing latency. As a result, analytics workflows remain uninterrupted, alarm generation is not delayed, and recording continuity is preserved.

Real-Time AI-Based Video Analytics

Each camera runs an independent analytics engine supporting capabilities such as object detection and classification; human, vehicle, and face analytics; multi-target tracking; identity-based person tracking; zone intrusion and line-crossing detection; crowding and density analysis; and occupational safety–oriented violation detection.

The analytics engine can dynamically switch between low-latency, accuracy-focused, or resource-efficient operating modes based on scene density and the current state of system resources.

AI Analysis on Historical Recordings (Retrospective AI)

IBBVision DLN 64 is not limited to live video analytics; it also performs AI-based scanning on historical video recordings. Users can retrospectively query where and how many times a specific person appeared, which cameras a particular vehicle passed through, or where a face was detected within specific time intervals.

Instead of reprocessing archived footage from scratch, the system utilizes feature vectors generated during recording. This vector-based search approach accelerates post-incident investigations, enables direct access to critical moments, and significantly reduces the need for manual video review.

Multi-Layer Security and Behavior Analytics

Within its core model package, the system supports a wide range of object and alarm classes under security analytics, occupational health and safety, and area utilization analytics. These include suspicious object and abandoned item detection; recognition of abnormal behaviors such as running or fighting; unauthorized area access; personal protective equipment detection; risky movement recognition; vehicle–pedestrian classification; reverse-direction movement detection; and crowd/queue analysis.

The alarm generation layer evaluates events by camera, time, and zone, prioritizes alarms based on severity scores, and can trigger automated recording workflows when required.

Custom Model Development and Institutional Integration

IBBVision DLN 64 allows the integration of up to 20 custom AI models tailored to the specific risks and operational needs of different institutions and sectors. Institution-specific scenarios—such as surface deformations in municipal environments, incorrect part placement on production lines, hazardous area approaches in energy sites, or suspicious package types in shopping malls—can be incorporated into the system.

The model lifecycle includes data sampling, label validation, edge-oriented model compilation, and continuous monitoring of performance metrics. Models are integrated into the platform using version-controlled deployment.

ROI (Region of Interest)–Based Prioritization

By defining regions of interest for each camera, analysis frequency can be increased in critical areas while computational load is reduced in non-essential regions. This approach balances resource consumption, lowers false alarm rates, and enhances the effectiveness of security coverage in critical zones.

Alarm and Incident Management Layer

Alarms generated by the system are supported by visual overlays, real-time on-screen operator notifications, audible alerts, automatic event recording, and rapid access to incident moments (rewind and jump-to-event functionality). Alarm flows are prioritized by severity score, tasks can be assigned among operators, and alarm histories can be reported.

Reporting and Analytics Monitoring Dashboard

IBBVision DLN 64 does more than store recordings; it generates meaningful data for security and operations teams. Event and alarm distributions, detection accuracy rates, camera-level analytics intensity, model performance metrics, and scene-based risk maps can be monitored through the system.

These insights are used as decision-support mechanisms for preventive security planning, camera placement optimization, and workforce planning processes.

Technical Architecture and Processing Pipeline

The system architecture consists of RTSP stream ingestion, transfer to time-stamped frame queues, execution of a parallel AI inference pipeline, object and behavior analysis, generation and storage of embedding data alongside metadata, and concurrent operation of the alarm engine.

The infrastructure supports a multi-process inference architecture, camera-level CPU/GPU resource allocation, parallel queue processing, runtime model selection management, and latency-focused adaptive frame processing.

Integration and Compatibility

The system provides full RTSP compatibility with IP cameras from all manufacturers. It can integrate with third-party VMS and CMS solutions and supports REST-based service outputs as well as webhook-based alarm integrations.

Application Areas

Corporate campuses and large-scale facilities, municipal and public spaces, multi-entrance buildings and shopping malls, production and quality control lines, energy sites, and critical infrastructure environments are among the primary application areas targeted by IBBVision DLN 64.

Advanced Security Infrastructure • 4 IP Camera Support • 20 Custom Model Slots

IBBVision DLN4 is a professional-grade AI-powered video analytics solution developed for corporate environments with high security demands. It processes and stores real-time video streams from four separate IP cameras simultaneously. Thanks to its enhanced analytics capacity, it enables the synchronized monitoring of broader areas. The system also offers high-level customization through the integration of up to 20 user-trained object detection models.

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Features

• 4-Camera Support: Simultaneous video stream processing from four IP cameras across multiple zones.

• Real-Time Multi-Camera Analysis: Independent and parallel processing of each camera feed.

• 80 Object and Alarm Categories: Covers security, occupational safety, traffic, and general analytics classifications.

• Up to 20 Custom AI Models: Enables training and deployment of custom models tailored to organization-specific threats or objects.

• Intelligent Alarm Management: Instant visual and audio alerts with automatic recording on suspicious events.

• Comprehensive Reporting: Access to event history, analytics outcomes, and AI model performance tracking.

• ROI (Region of Interest) Support: Priority-based analysis of predefined critical zones within the camera view.

• User-Friendly Architecture: Centralized control and configuration from a unified interface.

• Full RTSP Compatibility: Seamlessly integrates with all RTSP-compliant IP cameras.

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Application Areas

• Corporate campuses and large-scale facilities

• Municipality-managed zones and open parking lots

• Multi-entrance buildings and shopping malls

• Manufacturing sites and quality control zones

• Critical infrastructure and energy plants