Network Video Recorders (NVRs) - Expert commentary

Enhancing video surveillance data storage with active archive solution
Enhancing video surveillance data storage with active archive solution

By 2020, video surveillance using fixed, body and mobile cameras is expected to capture an astounding 859 PB of video daily. Increasing retention regulations and higher resolution cameras, are forcing the video surveillance industry to reassess its approach to data storage. Large capacity primary storage tends to be expensive to procure and costly to implement – especially without a sound architecture that can balance storage performance levels with the speed of access needed to recall video footage. Active archive strategy These challenges are thrusting storage tiers to the forefront of system design. Storage tiers in video surveillance had previously meant simply using a separate archive or attaching add-on capacity directly to network video recorders. Many of the new storage options designed for video surveillance are pulling together different storage tiers into a single storage architecture Many of the new storage options designed for video surveillance are pulling together different storage tiers (and in some cases storage media) into a single storage architecture, such as an active archive solution. This balance can be achieved with an active archive strategy that automates migration of data between different storage types, to ensure the data is on the correct storage type at the correct time to meet performance and retention requirements without blowing the budget. This approach also ensures ease of access while automatically moving content from more expensive tiers of storage to more cost-effective long-term tiers of storage. This allows for greater efficiencies in how recorded footage is treated throughout its lifecycle. In some cases, it includes moving data from edge devices to centralised storage, and then to the public cloud. Scalable video storage solutions As storage demands have increased, video management vendors have turned to storage specialists for solutions that can accommodate large numbers of high-resolution video files, metadata associated with the footage for easy searching, along with much needed scalable solutions. In terms of video management software, this means the integration of video content from different storage types, tiers and physical locations is required, and which considers the performance profile of each storage type. With an active archive solution, video content is searchable and accessible directly by the end users regardless of where it is stored. Deploying an active archive solution enables surveillance users to reduce the complexity and costs of managing data for long term retention As seen in many product categories, camera and storage vendors continue to provide extremely competitive offerings. But, storage-specific solutions for video surveillance have lagged behind the roadmaps for video equipment and, as more and more cameras have entered the market, less attention has been placed on video storage capacities. Tiered storage strategy The surveillance industry has evolved considerably from the days of the 8mm video recorder; however, enterprise storage solutions will be forced to evolve further to cope with changing storage retention requirements. Video storage is quickly becoming one of the most expensive parts in a surveillance solution, but there is hope. Deploying an active archive solution will enable surveillance users to reduce the complexity and costs of managing from terabytes to petabytes of data for long term retention. By finding a storage solution that delivers the ability to implement a tiered storage strategy, users can adhere to regulation requirements to retain video footage and meet their safety and security objectives, while also significantly reducing storage costs and operational expenses.

Video surveillance must modernise in storage, recording and on-demand access
Video surveillance must modernise in storage, recording and on-demand access

Dollars spent by video surveillance customers must go towards ensuring high-availability capture, storage and on-demand access to live and archived video. Reaching this goal mandates high-availability of independent components – camera, network, storage (edge, external), internet connectivity, display, all Video Management Software (VMS) components and an architecture that can take advantage of this. In this note, we focus on seeing our way through to a video surveillance architecture, that provides high availability storage, access to live and stored video content. Of all options available to store recorded video, edge recording is the only one that is unaffected by network failure Edge recording Of all options available to store recorded video, edge recording is the only one that is unaffected by network failure. This makes edge storage a must-have. But, this has some limitations at present: Edge storage capacity is limited. Edge media has a short lifetime, rated only for thousands of hours of continuous recording. Most cameras are not secure and physical damage to the camera could lead to catastrophic loss of edge stored content. As storage and compression technology evolve, the constraints imposed by (1) and (2) could go away. However, securing cameras will continue to be a barrier for most installations. Secure external storage It is thus imperative to also store video in secure external storage. Such an architecture uses edge storage to fill in content gaps created by network, external storage outages. As edge storage technology improves, larger gaps can be filled in, but one will always need external storage. By our definition, ‘external storage’ is a solution stack that includes storage media and all software (including VMS) that provide access to this storage. Access to live and archived video Access to live video can either be met by external storage or directly by the camera Every surveillance solution needs to provide access to live and archived video. Access to live video can either be met by external storage or (and) directly by the camera. All things being equal, having the camera directly provide live video access, is a higher-availability solution. There is dependence on fewer components in the chain. Solutions in the market use one of the above two approaches for access to live video. Due to limited capacity and low physical security of edge storage, it makes sense at present, to have external storage meet all requests for archive video. Thus, we are led to an architecture that has heavy dependence on external storage. Dual-recording For high-availability, external storage must be architected with redundancy. Ideally, independent components that make up external storage – storage media, associated hardware and software (including VMS components), should be individually redundant and have smart interconnectivity. However, solutions in the market rigidly tie these components together. Failure of a single component causes failure of external storage. For e.g. hardware failure of a server causes VMS component failure AND storage failure. DR provides a smart way to provide high-availability for external storage For these solutions in the market, high-availability is achieved by having additional external storage units that step-in during outages of primary units. If these additional units continuously duplicate primary units, access gaps are minimised, and archive access is un-affected during primary unit outages. This is the idea behind Dual-Recording (DR).  To meet cost budgets, these additional units can be configured to store subsampled (framerate, resolution) video content. A small number of additional units can support concurrent outages of all primary units. A few-to-many redundancy. Rising need for dual-recording Most cameras cannot be physically secured, and video content produced by a camera must be stored externally. Many VMS solutions use external storage to service live video access requests. Edge storage limitations impose restrictions on edge archive access at present. So, external storage is used to service requests for archive access too. Thus, a surveillance system ends up being over-dependent on external storage. DR provides a smart way to provide high-availability for external storage. As edge storage improves, it will be able to service archive access requests. VMS software will need to evolve, to use this capability smartly.

Artificial intelligence is changing intrusion detection dynamics in the security industry
Artificial intelligence is changing intrusion detection dynamics in the security industry

With the ever-growing availability of video data thanks to the low cost of high-resolution video cameras and storage, artificial intelligence (AI) and deep learning analytics now have become a necessity for the physical security industry, including access control and intrusion detection. Minimising human error and false positives are the key motivations for applying AI technologies in the security industry. What is artificial intelligence? Artificial intelligence is the ability of machines to learn from experience using a multi-layer neural network, which mimics the human brain, in order to recognise items and patterns and make decisions without human interference. The human brain is estimated to have 86 billion neurons; in comparison, the newest Nvidia GPU Volta has 21 billion transistors (the equivalence of a neuron), which offers the performance of hundreds of CPUs for deep learning.AI can learn continuously 24 hours per day every day, constantly acquiring, retaining and improving its knowledge In addition, unlike humans, AI can learn continuously 24 hours per day every day, constantly acquiring, retaining and improving its knowledge. With such enormous processing power, machines using Nvidia GPU and similar chips can now distinguish faces, animals, vehicles, languages, parts of speech, etc. Depending on the required complexity, level of details, acceptable error margin, and learning data quality, AI can learn new objects within as fast as a few seconds using Spiking Neural Network (SNN) to a few weeks using Convolution Neural Network (CNN). While both SNN and CNN offer advantages and drawbacks, they outperform tradition security systems without AI in terms of efficiency and accuracy. According to the research reports of MarketsandMarkets, the market size of perimeter intrusion detection systems is projected to increase from 4.12 billion USD in 2016 to 5.82 billion USD in 2021 at a Compound Annual Growth Rate (CAGR) of 7.1%. Meanwhile, the predicted market of AI in security (both cyber security and physical security) will grow from 3.92 billion USD in 2017 to 34.81 billion USD by 2025, i.e., with an impressive CAGR of 31.38%. Legacy perimeter intrusion detection systems Legacy perimeter intrusion detection systems (PIDSs) are typically set up with the following considerations: Geographical conditions: landscape, flora, fauna, climate (sunrise, sunset, weather conditions, etc.), whether there are undulations in the terrain that would block the field of view of cameras Presence or lack of other layers of physical protection or barriers Integration with other systems in the security network: camera, storage, other defensive lines (door, lock, alarm, etc.) Types of alarm triggers and responses System complexity: intrusion detection with various types of sensors, e.g., microwave sensors, radar sensors, vibration sensors, acoustic sensors, etc. Length of deployment Local regulations: privacy protection, whether the cameras/sensors must be visible/hidden/buried, etc., electromagnetic interferences that may affect other systems such as oil rigs or power plants Human involvement: on-site personnel arrangement, human monitoring, human action in response to alarms AI object detection can easily distinguish different types of people and objects Pain points and benefits of AI The conditions listed above correspond to certain requirements of an intrusion detection systems: minimal false alarm, easy setup and maintenance, easy integration, and stable performance.AI by nature is designed to learn, adapt itself and evolve to work in multiple conditions: it should be integrated with existing video recording systems  Minimal false alarms: False alarms lead to increased cost and inefficiency but are the main problem of PIDSs without AI technology, where animals, trees, shadows, and weather conditions may trigger the sensors. AI object detection can easily distinguish different types of people and objects, e.g., in a region set up to detect people, a car driving by, a cat walking by, or a person’s shadow will not trigger the alarm. Therefore, the amount of false alarms can be reduced by 70% to orders of magnitude. Easy setup and maintenance: Legacy PIDSs without AI must account for terrain, line of sight of cameras, sensor locations; any changes to the system would require manual effort to recalculate such factors and may disturb other components in the system. In contrast, AI PIDSs enable the system administrator to access the entire system or individual cameras from the control room, configure the region and object of interest in the field of view of cameras within minutes, and adjust with ease as often as necessary. Computing knowledge and even specific security training are not required to set up a secured PIDS with AI because AI PIDS is designed to relieve humans from knowing the inner working of machines. Easy integration with complementary technologies: Legacy PIDS without AI relies on physical technology, which are often proprietary and require complete overhaul of systems to function smoothly. On the other hand, AI by nature is designed to learn, adapt itself and evolve to work in multiple conditions, so AI PIDS is easily integrated with existing video recording (camera) and storage (NVR) systems. AI also eliminates the need for physical wireless or fiber-based sensors; instead, it functions based on the videos captured by cameras. Furthermore, AI enables easy and instantaneous combinations of multiple layers of defense, e.g., automatic triggering of door lock, camera movement focusing and access control as soon as a specified object is detected in the region of interest, all set up with a click of a button.  Stable performance and durability: Legacy PIDSs without AI requires complicated setup with multiple components in order to increase detection accuracy. More components mean a higher probability of malfunction in the system, including exposure to damages (e.g., sensors can be destroyed) and delay in detection, while human monitoring is inconsistent due to human fatigue (studies have shown that a person can concentrate in mundane tasks for only up to 20 minutes, and the attention span decreases even more rapidly when humans are faced with multiple items at once, e.g., multiple camera monitoring screens). AI significantly reduces, if not completely eliminates the need for human involvement in the intrusion detection system once it is set up. In addition, AI reduces the risk of system malfunction by simplifying the hardware sensors needed. Minimising human error and false positives are the key motivations for applying AI technologies in the security industry Additional benefits of AI in intrusion detection Artificial Intelligence is undeniably reshaping every business and weaving into every aspect of daily lifeMaximal detection capability: The most advanced AI intrusion detection system today provides an all-in-one solution to distinguish any combination of alarm-triggering criteria beyond perimeter protection. Using AI, the system administrator can configure as many zones with different settings and object of interests as necessary, which include detections for specific colors or attributes (e.g., person not wearing the required uniform or carrying food/drink), numbers and dwell time (e.g., group of more than 5 people loitering), or movements (e.g., cars moving faster than the speed limit). In addition, AI can accurately pinpoint the location of event occurrence by displaying the camera that records the event in near real time, i.e., with few-second delays. Lower security operation cost: By minimising the number of false positives and human involvement while maximising ease of use and stability, AI intrusion detection systems significantly decrease the total cost of ownership. Companies can reduce the large security personnel overhead and cost of complicated and expensive legacy PIDSs systems. McKinsey Global report in June 2017 shows that proactive AI adopters can realize up to 15% increase in profit margin across various industries. Artificial Intelligence is undeniably reshaping every business and weaving into every aspect of daily life. In security, legacy systems are giving way to AI-based systems, and the first enterprises to adopt AI-based systems will soon, if not immediately, benefit from such investment. By Paul Sun, CEO of IronYun, and Mai Truong, Marketing Manager of IronYun