We’re Tracking the Future of Security

Category: Video Surveillance & Imaging

Cameras, video management software (VMS), AI video analytics and imaging technologies used to monitor and secure physical spaces.

  • AI Video Analytics: What Actually Works?

    AI Video Analytics: What Actually Works?

    Marketing around AI video analytics often implies a single, uniformly capable technology. In practice, “AI video analytics” covers a range of distinct tasks with very different levels of real-world maturity, from tasks that are now routinely reliable to others that remain error-prone outside controlled conditions. Understanding that range matters for anyone deciding what to actually deploy and trust.

    Well-Established: Object Classification and Counting

    Detecting and classifying broad object categories, such as person, vehicle or bag, is now a mature capability across most commercial video analytics products, built on deep-learning models trained on large, diverse image datasets. People counting and basic line-crossing or zone-intrusion detection built on this foundation are generally reliable in typical lighting and camera-placement conditions, which is why these features have become standard rather than premium additions on many camera and VMS platforms.

    Increasingly Reliable: License Plate Recognition

    Automatic license plate recognition has matured considerably and performs well under favorable conditions: adequate lighting, a reasonably direct camera angle and moderate vehicle speed. Performance still degrades with poor lighting, extreme angles, dirty or damaged plates, and regional plate formats the underlying model was not trained on, which is why plate-recognition systems are typically deployed with purpose-selected cameras and lenses rather than repurposed general-surveillance cameras.

    Mixed Results: Behavioral and Anomaly Detection

    Detecting behaviors such as loitering, fighting, or a person falling is harder than classifying static objects because it requires interpreting motion and context over time, and there is far less standardized training data for rare or unusual events than for common objects like people and cars. Vendors have made real progress here, but false-positive and false-negative rates for behavioral analytics remain noticeably higher than for basic object detection, and performance is more sensitive to camera angle, crowd density and scene complexity.

    Still Immature for Many Deployments: Facial Recognition at Scale

    Facial recognition accuracy has improved substantially in laboratory testing, but real-world performance depends heavily on image quality, angle, lighting and the size and diversity of the reference database being matched against. Independent testing bodies, including the U.S. National Institute of Standards and Technology, have documented accuracy differences across demographic groups for some algorithms, which is part of why facial recognition deployment in public and semi-public spaces continues to draw closer regulatory scrutiny than other forms of video analytics.

    The Common Thread: Conditions Matter More Than Marketing

    Across all of these categories, the gap between vendor demonstration performance and field performance usually comes down to conditions: camera placement, lighting, resolution, frame rate, scene complexity and how closely the deployment environment matches the data the underlying model was trained on. Security teams evaluating AI video analytics get more reliable results by piloting a product in their actual environment before wide deployment than by relying on vendor-reported accuracy figures alone, since those figures are typically generated under favorable test conditions.

    FAQ

    Which AI video analytics feature is most reliable today? Basic object classification — distinguishing people, vehicles and similar broad categories — is generally the most mature and consistently reliable analytics capability across vendors.

    Why do vendor accuracy claims sometimes not match real-world results? Vendor figures are often measured under favorable test conditions. Real deployments introduce variables like lighting changes, camera angle, weather and scene clutter that reduce accuracy compared with controlled testing.

    Should organizations pilot AI analytics before full deployment? Yes. Because performance is highly condition-dependent, testing analytics in the actual deployment environment is the most reliable way to validate accuracy before committing to a wide rollout.

  • From CCTV to Video Intelligence: The Evolution of Surveillance

    From CCTV to Video Intelligence: The Evolution of Surveillance

    The term CCTV, short for closed-circuit television, describes a technology that has changed almost beyond recognition since it first appeared in commercial security. What began as a closed loop of cameras feeding a bank of monitors and videotape recorders has become a distributed, searchable intelligence system. Tracing that evolution helps explain why the industry increasingly talks about “video intelligence” rather than simply surveillance.

    The Analog Era: Recording Without Searching

    Early CCTV systems recorded continuously to videotape, which had to be physically swapped, stored and, when needed, reviewed in real time by fast-forwarding through hours of footage. There was no way to search for a specific event other than knowing roughly when it occurred and manually scrubbing through the tape. Coverage was also limited by cost and cabling; each camera required a dedicated coaxial run back to a central recorder.

    Digital Video Recording and IP Cameras

    Digital video recorders (DVRs) replaced tape with hard drives, letting operators jump to a timestamp instantly rather than fast-forwarding physical media. The shift to IP cameras that transmit over standard computer networks further loosened the physical constraints of analog systems, allowing cameras to be added, moved or networked across sites without dedicated coaxial cabling, and enabling remote viewing over the internet for the first time.

    Video Management Systems Bring Structure

    As camera counts grew, video management system (VMS) software became necessary to organize feeds, manage storage, control user access and provide a single interface for monitoring and playback across potentially hundreds of cameras. VMS platforms introduced features like camera health monitoring, role-based access for operators, and integration with access control and alarm systems, turning a collection of individual cameras into a managed security infrastructure.

    Analytics Turn Video Into Searchable Data

    The more recent shift toward video intelligence comes from AI-based video analytics that extract structured information from footage: object classification, license plate recognition, people counting, and behavioral patterns such as loitering or wrong-way movement. Instead of reviewing footage sequentially, an investigator can now search by object type, color, direction of travel or, increasingly, natural-language description, retrieving relevant clips in seconds rather than hours. This is the functional definition of video intelligence: video that has been indexed and made queryable, rather than simply archived.

    From Reactive Review to Proactive Alerting

    Video intelligence has also shifted surveillance from a largely reactive tool, used mainly to investigate incidents after the fact, toward a proactive one that can generate real-time alerts for defined conditions, such as a person entering a restricted zone after hours or a vehicle stopped in a fire lane. That shift depends on the analytics running continuously against live video rather than only against recorded footage, which is part of why edge AI processing on cameras themselves has become an important complement to server-based video intelligence platforms.

    FAQ

    Is CCTV an outdated term? The term persists in everyday use, but modern systems typically use IP-based digital cameras, networked recording and AI-based analytics rather than the closed coaxial-cable loops the term originally described.

    What is the difference between a VMS and video analytics? A VMS manages recording, storage, access and playback across cameras. Video analytics is software, often running within or alongside a VMS, that interprets the content of the video to detect and classify objects and events.

    Does video intelligence require replacing existing cameras? Not always. Many video intelligence features can run as software layered on top of existing IP cameras and VMS platforms, though the accuracy and range of available analytics generally improve with cameras that include onboard processing.

  • Edge AI Cameras: How Cameras Became Intelligent Sensors

    Edge AI Cameras: How Cameras Became Intelligent Sensors

    For most of the history of video surveillance, a camera’s job ended at capturing an image. Analysis, if it happened at all, took place later, either by a person reviewing footage or by a server crunching video after the fact. Edge AI has changed that division of labor, moving detection and classification directly onto the camera itself, at the moment the image is captured.

    What ‘Edge’ Means in This Context

    In computing generally, the “edge” refers to processing that happens close to where data is generated, rather than in a centralized data center or cloud. For a security camera, that means running analytics on a chip inside the camera housing rather than streaming raw video to a server or the cloud for processing. The camera itself decides, in real time, whether a frame contains a person, a vehicle, a package left behind, or a fence line being crossed.

    From Motion Detection to Object Understanding

    Early “smart” cameras offered motion detection based on pixel change between frames, a technique that could not distinguish a person from a blowing tree branch or a passing cloud shadow. The generation of onboard neural processing units now built into many commercial cameras allows the device to run a trained deep-learning model directly on the video stream, classifying objects by type and often by attributes such as clothing color or vehicle type, without sending the video anywhere for that first pass of analysis.

    Why Processing at the Camera Matters

    Doing this work on the camera rather than centrally offers several practical advantages. It reduces the bandwidth needed to move video across a network, since only metadata or short alert clips, rather than continuous full-resolution streams, may need to travel to a central system. It also cuts the latency between an event happening and an alert being generated, which matters for use cases like perimeter intrusion or wrong-way vehicle detection where seconds count. Finally, running detection locally can reduce dependence on a live network or cloud connection, letting a camera continue generating alerts even if connectivity to a central server is temporarily lost.

    Metadata as the New Output

    Perhaps the more significant shift is that edge AI cameras produce structured metadata alongside video: timestamps, object classifications, bounding boxes, and sometimes attributes like direction of travel. That metadata can be indexed and searched far more efficiently than raw video, which is what makes features like natural-language video search and cross-camera object tracking practical at scale. In effect, the camera has become a sensor that reports both an image and a description of what it saw, rather than a device that only reports an image.

    Limitations Worth Understanding

    Edge processing is not a universal upgrade. Cameras with onboard AI chips generally cost more than conventional models, and the accuracy of onboard detection depends heavily on the quality and diversity of the training data behind the model, along with factors like camera placement, lighting and weather. Organizations evaluating edge AI cameras typically still pair them with a video management system capable of aggregating and correlating metadata across many devices, since a single camera’s local intelligence is most useful when it feeds into a broader security picture.

    FAQ

    Do edge AI cameras still send video to a server? Usually yes, for recording and human review, but the initial detection and classification happen on the camera, which can reduce how much video needs to be analyzed centrally in real time.

    Are edge AI cameras more accurate than server-based analytics? Not inherently. Accuracy depends on the underlying AI model and training data, not simply on where the processing happens. Edge processing is primarily a bandwidth, latency and resilience advantage.

    Can existing cameras be upgraded to edge AI without replacement? Some manufacturers offer firmware updates that add basic analytics to existing camera lines, but full onboard neural processing generally requires camera hardware built with a dedicated AI chip.

  • IQSIGHT Names Michael Schulte Chief Executive Officer

    IQSIGHT Names Michael Schulte Chief Executive Officer

    IQSIGHT, the video security business formerly known as Bosch Video Systems, has appointed Michael J. Schulte as chief executive officer, effective Sept. 1, 2026. Schulte will lead the company’s global operations and report to Chairman Steve Shine.

    A Veteran Operator Takes the Helm

    Schulte brings more than 30 years of leadership experience across technology, industrial and private-equity-backed businesses, including senior roles at Safe Fleet, Atkore International, Danaher Corporation and Boston Consulting Group. He holds an MBA from Harvard Business School.

    “IQSIGHT has a strong heritage, outstanding people and a compelling strategy,” Schulte said. “I am excited to join the team and build on the excellent work already underway. Together, we will focus on execution, innovation and creating even greater value for our customers, partners and employees.”

    Positioning for the Next Growth Phase

    Shine, who remains chairman and will work with Schulte on strategic direction, said the appointment follows months of work with the leadership team to define IQSIGHT’s direction following its transition from Bosch Video Systems.

    “Over the past months, we have worked closely with the leadership team to define a clear strategic direction for IQSIGHT and position the business for its next phase of growth,” Shine said. “With that foundation in place, this is the right time for Michael to take the helm.”

    The leadership change comes as IQSIGHT continues to build a standalone identity in the video security market, aiming to maintain continuity for existing customers and channel partners while pursuing global expansion.

  • Geutebrück Enters New Ownership Phase With IBEX and Merantix Momentum Investment

    Geutebrück Enters New Ownership Phase With IBEX and Merantix Momentum Investment

    German video security specialist Geutebrück is entering a new corporate phase after receiving a majority investment from IBEX Wachstumspartner, alongside European AI group Merantix Momentum, according to Security Info Watch and confirmed by law firm Schmitz Knoth, which advised Geutebrück’s shareholders on the transaction. The deal closed on August 31, 2026.

    New Leadership and International Ambitions

    As part of the transition, Tobias Huemmerich will take over management of the company, with plans to further internationalize the business and expand its customer offering, according to Security Middle East. Geutebrück has operated in the video security market for more than 55 years and has built a reputation as a provider of video management software and hardware for public-sector and highly critical infrastructure clients.

    Merantix Momentum Brings AI Expertise

    Merantix Momentum, described as Europe’s leading AI group, is joining the investment specifically to help Geutebrück develop new AI-based applications, according to the companies. Geutebrück has said it sees opportunities to extend its video management platform beyond traditional security use cases, including the potential use of anonymized video data to identify workflow efficiencies and improve occupational safety.

    Part of a Broader Consolidation Trend

    The investment adds to a string of ownership changes across the video surveillance sector as manufacturers seek capital and AI expertise to keep pace with larger competitors. IBEX Wachstumspartner, an owner-managed investment firm focused on succession situations at German medium-sized businesses, said the deal reflects its strategy of pairing growth capital with digitalization expertise for established technology companies entering a generational transition.

  • Ring Rolls Out TAKE Encryption to Limit Who Can Access Home Security Camera Footage

    Ring Rolls Out TAKE Encryption to Limit Who Can Access Home Security Camera Footage

    Amazon’s Ring announced a new default encryption system for its security cameras and video doorbells on August 26, 2026, called TAKE — short for Throw Away the Key Encryption — designed to limit ongoing access to customer video, including by Ring itself and by law enforcement without a user’s consent, according to the company’s announcement and reporting from The Verge and CEPRO.

    How TAKE Works

    Under the new system, each camera encrypts its video using unique keys that rotate roughly every five minutes. A temporary copy of each key is held in what Ring calls a secure enclave in the cloud, long enough to power features such as smart alerts, video search and AI-generated video descriptions, after which the company says it permanently deletes its copy of that key. Ring has framed the approach as a middle ground between full end-to-end encryption, which would block many of its AI-driven features outright, and its previous architecture, which gave Ring’s cloud infrastructure more persistent access to footage.

    Rollout Timeline

    TAKE is scheduled to become the default encryption setting for all Ring customers worldwide in phases starting in September 2026, according to the company’s announcement.

    Why the Framing Matters

    The change follows years of scrutiny over Ring’s video-sharing practices, including a 2023 FTC settlement over unauthorized employee and law-enforcement access to customer footage, and comes as its former data-sharing partner Flock Safety faces its own wave of municipal contract cancellations over surveillance and privacy concerns. Privacy researchers note that because Ring — not an independent, published standard — designed the key-deletion process itself, customers still have to trust Amazon’s implementation rather than verify it independently, a caveat also raised by outlets covering the announcement.

  • Ambient.ai Adds Agentic Video Monitoring and Case Management Tools Ahead of GSX 2026

    Ambient.ai Adds Agentic Video Monitoring and Case Management Tools Ahead of GSX 2026

    Ambient.ai, a physical security AI vendor, announced new agentic capabilities across its platform on August 26, 2026, adding AI agents that continuously monitor camera feeds and automated case-management tools that assemble scattered video clips into a single incident timeline, according to the company’s announcement carried via PR Newswire and confirmed by Security Systems News.

    What’s New

    The centerpiece of the release is an AI agent Ambient.ai describes as continuously monitoring every connected camera and surfacing the events operators are most likely to need to see, rather than requiring security teams to actively watch banks of live video feeds. Alongside it, the company introduced a new case-management workflow intended to turn clips from multiple cameras and time windows into a single connected incident narrative, cutting down the manual work of stitching together footage during investigations. Ambient.ai also said it made infrastructure upgrades to support the new features at scale.

    Timed to GSX 2026

    The company plans to demonstrate the new Agentic Video Wall and case-management tools live at its booth (#3923) during GSX 2026 in Atlanta, positioning the release as part of a broader wave of “agentic” AI marketing across the physical security industry this year, as vendors compete to move video analytics from passive alerting toward autonomous monitoring and response.

    Why It Matters

    The announcement reflects a broader industry shift already visible in the growing adoption of AI-assisted video management platforms, where vendors are racing to differentiate on how much investigative and monitoring work their software can take on autonomously rather than leaving it to human operators watching screens.

  • Parking Facility and Garage Security Technology

    Parking Facility and Garage Security Technology

    Parking facilities present a security profile shaped by the tension between convenience and control: operators need to move large volumes of vehicles in and out efficiently while protecting parked vehicles, deterring crime in low-visibility areas, and managing liability in spaces that are frequently unstaffed for long stretches of the day.

    Automated Access Control and License Plate Recognition

    Modern parking access control has largely moved away from ticket-based entry toward license plate recognition systems that identify vehicles automatically at entry and exit, matching them against reservations, permits or payment records without requiring drivers to stop and interact with a gate or attendant. Automatic barrier gates paired with vehicle detection loops and LPR cameras allow facilities to enforce access restrictions and capacity limits with minimal staffing, while maintaining an auditable record of every vehicle movement through the facility.

    Video Surveillance in Low-Visibility Environments

    Parking structures present persistent lighting and sightline challenges, particularly in multi-level garages with support columns, stairwells and dimly lit corners that create natural blind spots. Camera placement strategy in garages typically prioritizes chokepoints — entry and exit lanes, elevator lobbies, stairwell doors and pedestrian walkways — supplemented by wide-area coverage of parking bays, with increasing use of AI-based analytics to detect loitering, unusual dwell time near parked vehicles, or people entering restricted areas after hours.

    Emergency Communication and Panic Infrastructure

    Because parking facilities are frequently used at hours when few other people are present, blue-light emergency phone towers and intercom call points remain a standard feature in many garages, giving pedestrians a direct, monitored line to security or law enforcement without needing a mobile device. Newer deployments increasingly pair these fixed call points with mobile safety apps that let users share their location and request an escort or check-in while walking to their vehicle.

    Vehicle and Asset Theft Prevention

    Beyond general surveillance, parking security technology addresses vehicle-specific theft risks, including catalytic converter theft and break-ins, through a combination of visible camera coverage, motion-activated lighting, and in some deployments, license-plate-linked alerting that flags when a vehicle associated with a prior incident re-enters the facility.

    Integration With Broader Building Security

    In commercial and residential developments, parking facility access control is frequently integrated with the building’s broader access control and video management systems, allowing a single credential to grant access to both the garage and the building above it, and giving security operations a unified view of a person’s movement from vehicle to building entry rather than treating the parking facility as a separate, disconnected system.

  • Axis Survey Finds Growing Public Comfort With Video Surveillance, But Privacy Expectations Remain High

    Axis Survey Finds Growing Public Comfort With Video Surveillance, But Privacy Expectations Remain High

    A new survey commissioned by Axis Communications finds that nearly six in ten U.S. adults are more comfortable with video surveillance today than they were five years ago, even as respondents continue to expect transparency, privacy safeguards and responsible use of AI in how that surveillance is deployed, according to reporting from SecurityInfoWatch.

    The Numbers

    The survey found that 58% of U.S. respondents said they are more comfortable with video surveillance in both public and private spaces than they were five years ago. Within that figure, 20% described themselves as significantly more comfortable, while a further 23% reported being somewhat more comfortable. Comfort levels varied depending on where surveillance is deployed, with respondents generally more accepting of cameras in transportation hubs, retail environments and workplaces than in more private or ambiguous settings.

    Comfort Is Conditional, Not Unconditional

    Despite the overall upward trend in acceptance, the research makes clear that public tolerance for video surveillance is not unconditional. Respondents continued to cite transparency about how footage is used and stored, clear privacy policies, and confidence that AI-driven video analytics are applied responsibly as prerequisites for their comfort. That combination — rising baseline acceptance paired with persistent conditions attached to it — mirrors a pattern security researchers have observed across other public-facing surveillance and biometric technologies as AI-powered analytics become standard in commercial video management platforms.

    Why It Matters for Operators

    For security integrators and end users, the findings suggest that expanding camera coverage or adding AI analytics capabilities is unlikely to trigger the public backlash it might have several years ago, provided organizations pair those deployments with clear signage, published data-retention policies and visible governance over how video and derived analytics are used. The survey adds to a growing body of industry research suggesting that public perception of video surveillance is shifting from a binary privacy-versus-security debate toward a more nuanced expectation of accountable deployment.

  • Independent Analysts Rank Genetec the Global Leader in Video Management Software for Another Year

    Independent Analysts Rank Genetec the Global Leader in Video Management Software for Another Year

    Genetec has once again been named the world’s leading vendor of video management software, according to separate market analyses published this week by Omdia and Novaira Insights. Both research firms found that the Montreal-based physical security software company grew its market share in 2025, extending a leadership position it has held for several years running.

    Two Independent Rankings, One Conclusion

    Omdia’s Video Surveillance and Analytics Spotlight Service and Novaira Insights’ 2026 World Market for Video Surveillance Hardware and Software both placed Genetec first globally in VMS revenue. Novaira Insights additionally ranked the company number one worldwide in total video surveillance software and service agreements, excluding China, and credited it with the number one spot in the Americas for the fifteenth consecutive year. The research also found Genetec posted the largest share growth among the top ten vendors in EMEA and holds a top-three position in Asia Pacific outside China.

    “Growth in this market is not evenly distributed,” said Paul Bremner, practice leader for physical security at Omdia, in comments accompanying the report. “Buyers are consolidating around vendors that can deliver cloud, on-premises, and hybrid from a single open platform, and those vendors are taking share from ones that cannot.”

    Why the Category Keeps Consolidating

    Both analyst firms point to the same underlying dynamic reshaping the video management software market: enterprise and public-sector buyers are increasingly unwilling to run separate systems for cloud-hosted and on-premises cameras, and are instead favoring vendors whose platforms can span both deployment models without forcing a migration. Genetec has positioned its Security Center platform around that hybrid architecture for several release cycles, and the new rankings suggest the strategy is translating into measurable share gains rather than just marketing positioning.

    The video surveillance software and hardware market remains one of the larger and more competitive segments within physical security, spanning everything from small retail deployments to citywide public-safety camera networks. Independent share data from firms like Omdia and Novaira Insights is closely watched by integrators and end users evaluating long-term platform commitments, since VMS selection typically locks organizations into a vendor relationship spanning camera hardware, storage infrastructure and access control integrations for years.

    Neither report disclosed specific revenue figures for individual competitors, and Genetec did not release exact dollar-share numbers in its announcement of the findings.