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Analyze and interpret what’s happening in a scene in real time using advanced computer vision and AI/ML algorithms. Monitor 1,000+ camera streams simultaneously to detect unusual events and behaviors, safety risks, environmental hazards, and operational anomalies across cities, utilities, high-footfall areas, campuses, industries, transport hubs, public spaces, and critical infrastructure.
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Most CCTV monitoring solutions are built primarily for video recording and basic playback. With traditional frameworks and heavy reliance on manual monitoring, security teams struggle to interpret events as they unfold across large camera networks.
Video feeds, sensors, and operational dashboards often run in silos, limiting shared visibility and slowing coordinated decision-making during critical situations.
There is no real-time intelligence, so incidents are typically discovered after the fact, when opportunities to prevent or contain them are already lost.
Conventional analytics generate excessive false positives, which forces teams to spend more time validating alerts instead of responding to real risks.
Surveillance networks generate massive volumes of footage every day, and conventional software can’t scale efficiently to analyze and manage this growing stream of data in real time.
Concentio Scene Track is Scry AI’s intelligent video surveillance platform that transforms CCTV networks into AI-powered monitoring systems for public safety and urban operations. By combining computer vision, self-learning AI, and advanced analytics, the platform continuously analyzes live video streams to understand activities within a scene in real time.
It processes visual data to identify objects, behaviors, and patterns that do not constitute a “normal” scene in complex environments. This enables Scene Track to detect incidents, hazards, operational issues, and congestion across public spaces, urban facilities, and critical infrastructure. These insights trigger automated alerts and operational workflows, allowing teams to act quickly before incidents escalate and respond with accurate, data-driven insights.
Scene Track transforms CCTV networks into an intelligent monitoring layer that detects safety risks, operational anomalies, and environmental issues in real time. With automated alerts, incident workflows, and enterprise integrations, it helps organizations respond faster, reduce monitoring workloads, and improve operational oversight.
Scene Track connects directly to any existing CCTV network without requiring hardware changes or disruption. It ingests live or recorded video from a wide range of sources, including RTSP streams, ONVIF-compliant cameras, third-party VMS platforms, cloud APIs, and edge ingestion modules. This flexible architecture scales to 1,000+ simultaneous streams across enterprise facilities or city-wide networks.
AI-driven scene analysis uses advanced computer vision to monitor video streams and detect public safety incidents in real time. The platform automatically identifies events such as fighting or violence, vandalism and graffiti, abandoned objects, the presence of weapons or hazardous items, unusual behavior patterns, face mask or PPE compliance violations, and early signs of crowd panic or stampede risk.
Scene Track detects crowd density and occupancy across malls, stations, and large events, along with fire or smoke emergence, illegal dumping or waste overflow, and animal intrusion or wildlife presence. It identifies infrastructure faults, including surface cracks, sagging, and structural risks, plus queue buildup, congestion patterns, and sanitation or cleanliness conditions, supporting data-driven urban management.
The platform generates time-stamped video clips, bounding-box overlays that highlight detected objects or activities, and detailed event timelines that show how incidents unfold. This structured capture transforms raw detections into verifiable incident records. The platform also supports compliance-ready documentation through automated incident reports, audit logs, and secure evidence archives.
When a potential incident is identified, configurable rules trigger alerts and initiate predefined actions such as emergency workflows, city operations tasks, maintenance activities, or response team notifications. Alerts are routed to security personnel, operations teams, or emergency responders based on escalation policies and response priorities, enabling rapid, coordinated response.
Scene Track operates as part of a broader digital operations ecosystem rather than an isolated surveillance tool. It integrates with emergency response systems, municipal operations center dashboards, waste management systems, utility platforms, access control solutions, and enterprise systems like ERP, EAM, ticketing platforms, SCADA environments, and GIS layers. This enables automated responses and maintenance actions.
Our computer vision surveillance software delivers measurable improvements in safety and urban operations by automating detection and analysis for large-scale CCTV networks.
Faster Incident Detection through real-time AI video analysis
Lower Monitoring Workload with automated visual intelligence
Parallel Camera Streams supported without performance degradation
Multi-Scenario Detection across safety and urban operations domains
Scene Track integrates with existing CCTV cameras and VMS platforms without requiring any hardware replacement. Video streams are ingested through RTSP feeds, ONVIF protocols, VMS connectors, APIs, or edge processors. The platform then securely organizes both live and recorded video feeds, enabling continuous monitoring and AI-driven analysis across large camera networks.
Incoming video streams are analyzed in real time by multiple AI models operating in parallel. Each scene is evaluated to identify public safety risks such as fighting or violence, vandalism or graffiti, abandoned objects, weapons or hazardous items, panic or stampede indicators, face mask or PPE compliance violations, and unusual behavior patterns. The platform also detects critical urban and operational issues, including crowd density and occupancy levels, fire and smoke, illegal dumping or waste overflow, animal intrusion, infrastructure faults such as surface cracks or structural risks, queue build-ups, congestion, and sanitation or cleanliness issues across monitored environments.
Detected events are visualized within Scene Track dashboards through bounding-box highlights, heatmaps, incident timelines, and crowd movement patterns. These visualizations provide security teams and operations centers with a real-time view of activities across multiple locations, supporting faster situational awareness and coordinated response.
When incidents are detected, alerts and notifications are generated based on the event type. These alerts trigger emergency workflows, city operations tasks, or maintenance activities, and initiate response actions for the relevant teams. This allows security personnel, operations centers, and city authorities to take timely action and address incidents before they escalate.
Scene Track automatically records time-stamped video clips, event logs, and detection summaries for each incident. The platform generates incident reports, audit logs, and compliance documentation to support investigations, regulatory requirements, and operational oversight.
Scene Track integrates with a wide range of CCTV infrastructure and camera systems, supporting diverse surveillance platforms without requiring hardware replacement.
Engineered to meet global standards for video data protection, system interoperability, and responsible AI use across surveillance and operational environments.
Aligned with ISO 27001, SOC 2 Type II, and GDPR principles to protect video data integrity and ensure trusted data handling.
Supports ONVIF, RTSP, and open APIs for secure, scalable integration with existing surveillance and enterprise systems.
An ethical AI framework protects personal data, supports anonymization, and complies with global privacy regulations.
We are trusted by enterprises globally.
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Learn more about Scene Track’s features, deployment, integrations, and performance, and how AI-powered video intelligence supports safety and operational monitoring.
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