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Closed-circuit television, commonly known as CCTV, is now a critical component in contemporary security setups. These systems are deployed to observe and document occurrences in diverse settings, including residential areas, commercial establishments, and communal spaces.

Nonetheless, conventional CCTV setups face numerous obstacles, complicating the task of security teams in identifying and addressing potential threats.

Computer vision, or the ability of artificially intelligent systems to see like humans, has been a subject of increasing interest and rigorous research now. As a way of emulating the human visual system, the research in the field of computer vision purports to develop machines that can automate tasks that require visual cognition. However, the process of deciphering images, due to the significantly greater amount of multi-dimensional data that needs analysis, is much more complex than understanding other forms of binary information. This makes developing AI systems that can recognize visual data more complicated.

AI Integration in CCTV Systems

The escalation of video data from CCTV cameras presents a significant challenge, as conventional systems predominantly focus on recording and archiving, complicating the task of pinpointing specific events. This traditional approach necessitates that security personnel simultaneously monitor numerous video feeds, a process that is not only tedious but also prone to fatigue, diminishing their effectiveness. Crucially, this setup often results in the oversight of important events due to the impracticality of thoroughly monitoring all feeds.
In response, Artificial Intelligence (AI) technologies are being assimilated into CCTV systems to surmount these hurdles. AI-enhanced CCTV systems employ machine learning algorithms to scrutinize video data, enabling the real-time identification of anomalies and suspicious behaviors. These algorithms efficiently sift through vast amounts of video, isolating critical events and thus alleviating the burden on security staff.
Indoor/outdoor: Some security cameras are made specifically for the indoors and wont stand up to Mother Nature quite as well as their outdoor counterparts. If you plan to use cameras outside, make sure you purchase models that are weatherproof. Otherwise, water or dirt might interfere with the quality of your video feeds or, worse yet, break your camera. Security cameras might be minimally resistant to weather or completely weatherproof. Be sure to understand what level of protection from natural conditions your security camera offers.
The fusion of AI with CCTV systems marks a transformative phase in the security sector. These systems not only reduce the workload of security personnel by providing timely alerts of suspicious activities but also demonstrate versatility and effectiveness across various environments. With the ongoing evolution of AI, the future promises even more sophisticated CCTV systems, further elevating the standards of security and surveillance.

Video Surveillance Systems

Additionally, video analytics occurs locally through the SmartVision software, negating any need for additional payment. This local processing feature is crucial for users who prefer not to rely on cloud-based analytics solutions due to privacy or cost concerns.
With cutting-edge AI technology, including real-time object detection and facial recognition, the capabilities of computer-based CCTV systems are greatly enhanced, transforming them into powerful security tools. Real-time object detection allows for the instant identification and tracking of various objects within a video feed, which is crucial for scenarios requiring immediate recognition of objects such as vehicles, packages, or weapons.
The system also allows for significant space savings on local disks and in the cloud by utilizing artificial intelligence technology and reducing the number of frames per second in the absence of movement in the frame. Through object detection based on neural networks and time-lapse recording, SmartVision can reduce the size of the video archive by hundreds of times.
This technology uses complex algorithms to differentiate between numerous object types, providing detailed insights into the footage. Facial recognition adds a layer of personal identification, accurately recognizing and verifying individual faces against a database, invaluable in access control and identifying suspects. These technologies offer unparalleled advantages in both personal and commercial security settings. For personal use, they provide enhanced home security by alerting homeowners to unrecognized faces or objects. In commercial settings, they assist in access control, employee monitoring, and premises safety.
The Open Network Video Interface Forum (ONVIF) is a global standard for the interface of IP-based physical security products. Some cameras, however, either do not support ONVIF or do not adhere strictly to its standards. This can create additional challenges in managing and integrating these cameras into a unified system.

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Our company has been continuously striving to develop a stable system for working with IP cameras and processing video streams. The challenge is to create a universal solution that isnt tied to a specific IP camera manufacturer. The long hiatus in updates for SmartVision was due to our realization that to address performance and stability issues, we had to overhaul the system entirely. This meant moving away from relying on third-party libraries such as FFmpeg and VLC. In the upcoming version, we will implement our own video stream re-streamer and a player for video viewing. Soon, a new version of SmartVision will be released, aiming to tackle the long-standing challenges in the video surveillance software market, providing users with a reliable, efficient, and user-friendly solution.
To illustrate, conventional systems would require about 3.2 Tb of disk space for a continuous six-month archive from a single 1080p IP camera. Multiply this by the number of cameras in use, and the hard drive costs—operating 24/7—become quite significant. Furthermore, these systems also necessitate considerations for data backup and mirroring. With SmartVision , the utilization of object detection based on neural networks and time-lapse recording can reduce hard drive stress and decrease the video archive size by hundreds of times.
Most of the software bases itself on the ffmpeg libraries, which necessitate specific parameters and stream decoding depending on the IP camera model. This complexity means that for various camera manufacturers, one needs to specify particular parameters (fps, resolution, audio and video codecs, etc.). While VLC is a somewhat universal RTSP stream player, it has its limitations and drawbacks. FFMPEG is also a universal library, but using it for video re-streaming or altering video resolution demands significant computer resources.
VLC, while more or less universal as an RTSP stream player, has its limitations and drawbacks, especially in the realm of video surveillance. When using FFmpeg for video stream re-streaming or for adjusting video resolutions, significant computer resources are required. Designing proprietary libraries and players to display videos from a vast array of IP cameras is labor-intensive. As a result, many systems carry the disadvantages and problems of decades of open-source solution development.

Definition of CCTV Systems:

Closed-circuit television (CCTV) systems refer to a network of cameras and recording equipment used for surveillance purposes. Unlike broadcast television, the signal in a CCTV system is not openly transmitted but is instead monitored, typically for security and surveillance purposes. These systems are prevalent in various settings, capturing and recording footage that can be reviewed or monitored in real-time to enhance security, deter crime, and assist in investigations.

CCTV System