From passive to active video surveillance with the intelligent cameras from RIVA®
From passive to active video surveillance with the intelligent cameras from RIVA®

RIVA stand for "Realtime intelligent Video Analytics", an intelligent 3D-video analytic system which is already integrated in all RIVA cameras and encoders as a standard feature. The basic version VCA Presence continually tracks moving and stationary objects and persons and generates real-time alerts. Furthermore, camera tampering is detected. More VCA packages for special applications as well as several individual analytic filters can be purchased additionally. Thereby you always receive an analytic system for your individual needs at the best possible price. Due to the intuitive 3D graphic interface the video analytics can be set up quickly and easily. Thus, it takes only a few minutes until your camera and all functions of the analysis are ready to use. Once ready the video analytics tracks up to 100 targets and has up to 40 detection zones per camera. Also false alarms are reduced to an absolute minimum. Because a self-learning algorithm that automatically adapts environmental changes, ignores light changes due to cloud formation, artificial light and auto-iris camera operation as well as repetitive movements such as swaying trees or rippling water. Furthermore the video analytics adjusts to image degradation caused by rain, fog, dirty lens and low sun position glare. Another huge advantage is that personnel and storage costs as well as the data volume are reduced. Due to the special filters, the camera only transfers what is requested. Get to know the analysis filters and –packages from RIVA and let yourself be surprised by the benefits of the video analytics in a 10-part VCA series. In the next part you will learn which filters and cameras are necessary for an optimal perimeter protection.

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CCTV software - Expert commentary

What is AI Face Search? Benefits over facial recognition systems
What is AI Face Search? Benefits over facial recognition systems

When a child goes missing in a large, crowded mall, we have a panicking mom asking for help from the staff, at least a dozen cameras in the area, and assuming the child has gone missing for only 15 minutes, about 3 hours’ worth of video to look through to find the child. Typical security staff response would be to monitor the video wall while reviewing the footage and making a verbal announcement throughout the mall so the staff can keep an eye out for her. There is no telling how long it will take, while every second feels like hours under pressure. As more time passes, the possible areas where the child can be will widen, it becomes more time-consuming to search manually, and the likelihood of finding the child decreases. What if we can avoid all of that and directly search for that particular girl in less than 1 second? Artificial neural networks are improving every day and now enable us to search for a person across all selected camera streamsWith Artificial Intelligence, we can. Artificial neural networks are improving every day and now enable us to search for a person across all selected camera streams in a fraction of a second, using only one photo of that person. The photo does not even have to be a full frontal, passport-type mugshot; it can be a selfie image of the person at a party, as long as the face is there, the AI can find her and match her face with the hundreds or thousands of faces in the locations of interest. The search result is obtained in nearly real time as she passes by a certain camera. Distinguishing humans from animals and statues The AI system continuously analyses video streams from the surveillance cameras in its network, distinguishes human faces from non-human objects such as statues and animals, and much like a human brain, stores information about those faces in its memory, a mental image of the facial features so to speak. When we, the system user, upload an image of the person of interest to the AI system, the AI detects the face(s) in that image along with their particular features, search its memory for similar faces, and shows us where and when the person has appeared. We are in control of selecting the time period (up to days) and place (cameras) to search, and we can adjust the similarity level, i.e., how much a face matches the uploaded photo, to expand or fine-tune the search result according to our need. Furthermore, because the camera names and time stamps are available, the system can be linked with maps to track and predict the path of the person of interest. AI Face Search is not Face Recognition for two reasons: it protects people’s privacy, and it is lightweight Protecting people’s privacy with AI Face Search  All features of face recognition can be enabled by the system user, such as to notify staff members when a person of interest is approaching the store AI Face Search is not Face Recognition for two reasons: it protects people’s privacy, and it is lightweight. First, with AI Face Search, no names, ID, personal information, or lists of any type are required to be saved in the system. The uploaded image can be erased from the system after use, there is no face database, and all faces in the camera live view can be blurred out post-processing to guarantee GDPR compliance. Second, the lack of a required face database, a live view with frames drawn around the detected faces and constant face matching in the background also significantly reduces the amount of computing resource to process the video stream, hence the lightweight. Face Search versus Face Recognition AI Face Search Face Recognition Quick search for a particular person in video footage Identify everyone in video footage Match detected face(s) in video stream to target face(s) in an uploaded image Match detected face(s) in video stream to a database Do not store faces and names in a database Must have a database with ID info Automatically protect privacy for GDPR compliance in public places May require additional paperwork to comply with privacy regulations Lightweight solution Complex solution for large-scale deployment Main use: locate persons of interest in a large area Main use: identify a person who passes through a checkpoint Of course, all features of face recognition can be enabled by the system user if necessary, such as to notify staff members when a person of interest is approaching the store, but the flexibility to not have such features and to use the search tool as a simple Google-like device particularly for people and images is the advantage of AI Face Search.Because Face Search is not based on face recognition, no faces and name identifications are stored Advantages of AI Face Search Artificial Intelligence has advanced so far in the past few years that its facial understanding capability is equivalent to that of a human. The AI will recognise the person of interest whether he has glasses, wears a hat, is drinking water, or is at an angle away from the camera. In summary, the advantages of Face Search: High efficiency: a target person can be located within a few seconds, which enables fast response time. High performance: high accuracy in a large database and stable performance, much like Google search for text-based queries. Easy setup and usage: AI appliance with the built-in face search engine can be customised to integrate to any existing NVR/VMS/camera system or as a standalone unit depending on the customer’s needs. The simple-to-use interface requires minimal training and no special programming skills. High-cost saving: the time saving and ease of use translate to orders of magnitude less manual effort than traditionally required, which means money saving. Scalability: AI can scale much faster and at a wider scope than human effort. AI performance simply relies on computing resource, and each Face Search appliance typically comes with the optimal hardware for any system size depending on the customer need, which can go up to thousands of cameras. Privacy: AI Face Search is not face recognition. For face recognition, there are privacy laws that limits the usage. Because Face Search is not based on face recognition, no faces and name identifications are stored, so Face Search can be used in many public environments to identify faces against past and real-time video recordings. AI Face Search match detected face(s) in video stream to target face(s) in an uploaded image Common use cases of AI Face Search In addition to the scenario of missing child in a shopping mall, other common use cases for the AI Face Search technology include: Retail management: Search, detect and locate VIP guests in hotels, shopping centres, resorts, etc. to promptly attend to their needs, track their behaviour pattern, and predict locations that they tend to visit. Crime suspect: Quickly search for and prove/disprove the presence of suspects (thief, robber, terrorist, etc.) in an incident at certain locations and time. School campus protection: With the recent increase in number of mass shootings in school campuses, there is a need to identify, locate and stop a weapon carrier on campus as soon as possible before he can start shooting. Face Search will enable the authorities to locate the suspect and trace his movements within seconds using multiple camera feeds from different areas on campus. Only one clear image of the suspect’s face is sufficient. In the race of technology development in response to business needs and security concerns, AI Face Search is a simple, lightweight solution for airports, shopping centres, schools, resorts, etc. to increase our efficiency, minimise manual effort in searching for people when incidents occur on site, and actively prevent potential incidents from occurring. By Paul Sun, CEO of IronYun, and Mai Truong, Marketing Manager of IronYun

ONVIF Profile T and H.265: the evolution of video compression
ONVIF Profile T and H.265: the evolution of video compression

In today’s market, efficient use of bandwidth and storage is an essential part of maintaining an effective video surveillance system. A video management system’s ability to provide analysis, real time event notifications and crucial image detail is only as a good as the speed and bandwidth of a surveillance network. In the physical security industry, H.264 is the video compression format used by most companies. Some companies also employ H.264 enhancements to compress areas of an image that are irrelevant to the user at a higher ratio within a video stream in order to preserve image quality for more important details like faces, license plates or buildings. The H.265, H.264’s successor, will be increasingly used for compression in the future. Some companies are already using H.265 in their cameras and video management systems, while a host of other manufacturers are certainly preparing for its broader adoption in the years to come. Video compression technologies Reduced bandwidth and storage requirements are the primary benefits of video compression technologies Reduced bandwidth and storage requirements are the primary benefits of video compression technologies. In some cases, H.265 can double the data compression ratio of H.264, while retaining the same quality. Increased compression rate translates into decreased storage requirements on hard drives, less bandwidth usage and fewer switches – all of which reduce overall costs of system ownership. H.265 compression delivers a lower bitrate than H.264, which is relevant to end users and integrators because the lower bitrate reduces strain on hardware and can reduce playback issues. It’s very important that the compression format that is used is supported in all of the different components of a system: cameras, desktop computers on which the VMS is running and the VMS itself. It is also good for end users and integrators to understand the basics of video compression. Having a basic understanding of compression allows users to tweak settings to reduce bandwidth usage even more. Many cameras come with default settings that can be changed to ultimately reduce costs. ONVIF physical security In the physical security industry, ONVIF is working to incorporate into its specifications the use of new formats such as H.265 but is not directly involved in developing the compression standards themselves. With Profile T, the new ONVIF video profile released will employ a new media service that is compression agnostic. This means that it can support new video compression formats, including H.265, as well as new audio compression formats, with the ability to include new video and audio codecs as needed in the future without having to redesign its media service. In the physical security industry, ONVIF is working to incorporate into its specifications the use of new formats such as H.265 Standardisation organisations that are directly addressing new compression standards include the International Telecommunication Union (ITU), the Moving Picture Experts Group (MPEG) and a joint commission of the International Organization for Standardization (ISO)/International Electrotechnical Commission (IEC), which is addressing the coding of audio, picture, multimedia and hypermedia information. Other compression formats on par with H.264 and H.265 are being developed by companies such as Google. H.265 compression formats Using products that employ H.265 compression will reduce costs through bandwidth reduction, as will changing default settings on cameras, which are often conservative. Having a basic understanding of compression formats and how to tweak camera factory default settings also gives integrators the ability to further reduce bandwidth for added costs savings and increased system performance. These enhancements will analyse which parts of an image are most important and adjust local levels of compressions accordingly It is also worth noting that H.265 enhancements will likely be developed by camera manufacturers to further reduce bandwidth, as was the case with H.264. These enhancements will analyze which parts of an image are most important and adjust local levels of compressions accordingly. While H.265 itself is ready for prime time, its value as a tool for IP-based surveillance systems is dependent on support for the codec in all parts of the system – the VMS, server hardware, graphics cards and camera. Though widespread H.265 adoption is predicted, providers of these components are jumping on the H.265 bandwagon at different rates of speed. ONVIF is including support for H.265 in its new video profile, Profile T, because it believes it will become the most widely used compression format and ONVIF recognises the need to anticipate that migration as a future need of the industry. The new media service, which will be implemented with Profile T, will be future-proof in that when new compression formats are released in the future, ONVIF can adopt them very quickly. That flexibility will definitely help integrators.

How artificial intelligence (AI) is changing video surveillance today
How artificial intelligence (AI) is changing video surveillance today

There’s a lot of excitement around artificial intelligence (AI) today – and rightly so. AI is shifting the modern landscape of security and surveillance and dramatically changing the way users interact with their security systems. But with all the talk of AI’s potential, you might be wondering: what problems does AI help solve today? The need for AI The fact is, today there are too many cameras and too much recorded video for security operators to keep pace with. On top of that, people have short attention spans. AI is a technology that doesn’t get bored and can analyse more video data than humans ever possibly could.AI is a technology that doesn’t get bored and can analyse more video data than humans ever possibly could It is designed to bring the most important events and insight to users’ attention, freeing them to do what they do best: make critical decisions. There are two areas where AI can have a significant impact on video surveillance today: search and focus of attention. Faster search Imagine using the internet today without a search engine. You would have to search through one webpage at a time, combing through all its contents, line-by-line, to hopefully find what you’re looking for. That is what most video surveillance search is like today: security operators scan hours of video from one camera at a time in the hope that they’ll find the critical event they need to investigate further. That’s where artificial intelligence comes in. The ability of AI to reduce hours of work to mere minutes is especially significant when we think about the gradual decline in human attention spans With AI, companies such as Avigilon are developing technologies that are designed to make video search as easy as searching the internet. Tools like Avigilon Appearance Search™ technology – a sophisticated deep learning AI video search engine – help operators quickly locate a specific person or vehicle of interest across all cameras within a site. When a security operator is provided with physical descriptions of a person involved in an event, this technology allows them to initiate a search by simply selecting certain descriptors, such as gender or clothing colour. During critical investigations, such as in the case of a missing or suspicious person, this technology is particularly helpful as it can use those descriptions to search for a person and, within seconds, find them across an entire site. Focused attention           The ability of AI to reduce hours of work to mere minutes is especially significant when we think about the gradual decline in human attention spans. Consider all the information a person is presented with on a given day. They don’t necessarily pay attention to everything because most of that information is irrelevant. Instead, they prioritise what is and is not important, often focusing only on information or events that are surprising or unusual. Security operators scan hours of video from one camera at a time in the hope that they’ll find the critical event they need to investigate further Now, consider how much information a security operator who watches tens, if not hundreds or thousands of surveillance cameras, is presented with daily. After just twenty minutes, their attention span significantly decreases, meaning most of that video is never watched and critical information may go undetected. By taking over the task of "watching" security video, AI technology can help focus operators’ attention on events that may need further investigation. As AI technology evolves, the rich metadata captured in surveillance video will add even more relevance to what operators are seeing For instance, technology like Avigilon™ Unusual Motion (UMD) uses AI to continuously learn what typical activity in a scene looks like and then detect and flag unusual events, adding a new level of automation to surveillance. This helps save time during an investigation by allowing operators to quickly search through large amounts of recorded video faster, automatically focusing their attention on the atypical events that may need further investigation, enabling them to more effectively answer the critical questions of who, what, where and when. As AI technology evolves, the rich metadata captured in surveillance video – like clothing colour, age or gender – will add even more relevance to what operators are seeing. This means that in addition to detecting unusual activities based on motion, this technology has the potential to guide operators’ attention to other “unusual” data that will help them more accurately verify and respond to a security event. The key to advanced security When integrated throughout a security system, AI technology has the potential to dramatically change security operations There’s no denying it, the role of AI in security today is transformative. AI-powered video management software is helping to reduce the amount of time spent on surveillance, making security operators more efficient and effective at their jobs. By removing the need to constantly watch video screens and automating the “detection” function of surveillance, AI technology allows operators to focus on what they do best: verifying and acting on critical events. This not only expedites forensic investigations but enables real-time event response, as well. When integrated throughout a security system, AI technology has the potential to dramatically change security operations. Just as high-definition imaging has become a quintessential feature of today’s surveillance cameras, the tremendous value of AI technology has positioned it as a core component of security systems today, and in the future.