DLN 24 Deep Learning NVR

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.