March Networks CCTV Network / IP Cameras(8)
1/3 inch, Colour / Monochrome, 3MP resolution, Digital (DSP), Megapixel, 0.08 lux, Auto Iris, Direct Drive, 12 V DC, PoE, Motion Activated, 2048 x 1536, 30 fps, Back Light Compensation, White Balance, 1 ~ 1/10000s, Zoom, H.264, MJPEG, 10/100Mbps self-adaptive Ethernet (RJ-45), ONVIF S RTP, RTSP, RTCP, HTTP, HTTPS, IPv4, TCP, UDP, DHCP, ARP, LDAP, 3.5W, 200, 56 x 58 x 69, -10 ~ +50 C (14 ~ 122 F), Internet Explorer (9.0+), 0 ~ 90Add to Compare
3 MP resolution, Digital (DSP), 0.12 lux, Auto Iris, Direct Drive, PoE, High Speed, 4 ~ 6 mm, Bracket, 2048 x 1536, 25 fps, Inclusion DVR/ Web Server, Back Light Compensation, White Balance, Up to 30ms, Zoom, H.264, T/100BASE-TX PoE, RJ45 connector, 3.5 ~ 12 W, 840, IP67, IK10, -25 ~ +55 C (-13 ~ +131 F), 8 ~ 90, HDAdd to Compare
3 MP resolution, Digital (DSP), 0.08 lux, Auto Iris, Direct Drive, 12 V DC, PoE, Motion Activated, High Speed, Bracket, 2048 × 1536, 30 fps, Inclusion DVR/ Web Server, Back Light Compensation, White Balance, 1 ~ 1/10000s, Zoom, H.264, M-JPEG, 10/100Mbps self-adaptive Ethernet (RJ-45), ONVIF S RTP, RTSP, RTCP, HTTP, HTTPS, IPv4, TCP, UDP, DHCP, ARP, LDAP, 3.5 W, 200, -10 ~ +50 C (14 ~ 122 F), 0 ~ 90Add to Compare
3 MP resolution, Digital (DSP), 0.08 lux, Auto Iris, Direct Drive, 12 V DC, PoE, Motion Activated, High Speed, Bracket, 2048 x 1536, 30 fps, Inclusion DVR/ Web Server, Back Light Compensation, White Balance, 1 ~ 1/10000s, Zoom, H.264, M-JPEG, 10/100Mbps self-adaptive Ethernet (RJ-45), ONVIF S RTP, RTSP, RTCP, HTTP, HTTPS, IPv4, TCP, UDP, DHCP, ARP, LDAP, 3.5 W, 200, -10 ~ +50 C (14 ~ 122 F), 0 ~ 90Add to Compare
4 MP resolution, Digital (DSP), 0.01 ~ 0.1 lux, Auto Iris, Direct Drive, 12 V DC / 24 V AC / PoE, Motion Activated, High Speed, Ceiling, Wall, Wide Dynamic Range, 2688 x 1520, 25 fps / 30 fps, Inclusion DVR/ Web Server, Back Light Compensation, White Balance, 1/10000s, Zoom, H.264, M-JPEG, PTZ, RJ-45, 10/100Mbps Etherne, IPv4/v6, TCP/IP, UDP, RTP, RTSP, HTTP, HTTPS, DHCP, PPPoE, UPnP, QoS, ONVIF-S, 3.64 W, 330, 162 x 80 x 52, IP66, -30 ~ +55 C (-22 ~ +131 F), 10 ~ 90Add to Compare
4 MP resolution, Digital (DSP), 0 ~ 0.01 lux, Auto Iris, Direct Drive, 12 V DC / 24 V AC / PoE, Motion Activated, High Speed, Surface, Wall, Pole, Wide Dynamic Range, 2688 x 1520, 15 fps / 30 fps, Inclusion DVR/ Web Server, Back Light Compensation, White Balance, 1/10000s, Zoom, H.264, M-JPEG, PTZ, RJ-45, 10/100Mbps Ethernet, IPv4/v6, TCP/IP, UDP, RTP, RTSP, HTTP, HTTPS, DHCP, PPPoE, UPnP, QoS, ONVIF-S, 3.64 W, 790, 90 x 270, IP66, -55 ~ +55 C (-67 ~ +131 F), 10 ~ 90Add to Compare
3 MP resolution, Digital (DSP), 0.12 lux, Auto Iris, Direct Drive, PoE, High Speed, 3.6mm, 2048 x 1536, 30 fps, White Balance, Up to 30ms, Zoom, H.264 (MPEG-4 Part 10/AVC), RJ-45, 10/100Mbps Ethernet, IPv4/v6, TCP/IP, UDP, RTP, RTSP, HTTP, HTTPS, DHCP, ONVIF-S, 3.5 ~ 12 W, 860, IP67, -25 ~ +55 C (-13 ~ +131 F), Internet Explorer (10+), 8 ~ 90Add to Compare
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It's no secret that one of the next market segments to see exceptional growth in the United States is somewhat non-traditional: cannabis. The global cannabis market is projected to reach $60 billion by 2024, according to Ameri Research, fueled by the increasing legalisation and decriminalisation across much of the United States. It is estimated that 22 million pounds of marijuana are grown each year in the United States, with 80 percent coming from California, Tennessee, Kentucky, Hawaii and Washington, according to Mother Jones. Unlike other products, this commodity is valuable from the moment the seeds go in the ground to the exchanging of money for end-user products - and at every point in between. Within large greenhouses, 360-degree cameras that show a wide field of view are essential for cannabis protection From seedlings to selling, securing every point within the supply chain is vital to the assets being distributed, and companies are now realising how lucrative this endeavour can be. Critical to the success of the industry is keeping the merchandise secure and the workers safe. In this article, we explore each part of the supply chain within the cannabis market and address ways of implementing robust security measures. Plants, fields and greenhouses This is one industry where money actually grows on trees! When cannabis crops are planted either in greenhouses or in fields, security becomes critical, since the plants themselves are worth a significant amount of money. A single truckload can be worth hundreds of thousands of dollars, so securing the load is crucial to the process Producers don't want plants stolen – especially high-end varieties that garner a bigger profit when harvested and sold – and the size of the plants make theft a greater possibility. Video surveillance becomes vital at this point and can be used in a variety of ways. Within large greenhouses, single cameras that can cover a wide expanse of space, such as cameras that offer 360-degree views, are essential and can provide more coverage with less investment overall than traditional narrow field-of-view cameras. Advanced technology, such as unmanned aerial vehicles (or drones), are also being used in open fields in an effort to protect these plants. Comprehensive video surveillance becomes the main tool for thwarting cannabis theft and addressing incidents as they arise Transportation and protection Once the plants are mature enough to be harvested, they must be transported to a production facility where they are either dried or cured based on the needs of the grower, as well as processed and transformed into edible products to be sold at retail locations. There are already a range of companies that specialise in keeping these crop yields safe while they are transported: think Brinks armoured transportation used for cash, but for cannabis. A single truckload can be worth hundreds of thousands of dollars – if not more – so securing the load is crucial to the process. Losing one of these loads can lead to large-scale losses for a producer. Surveillance equipment that can withstand sanitation standards and power-washing is paramount for effective protection After being transported, cannabis must be processed. In these environments, where strict handling processes are in place, surveillance equipment that can withstand sanitation standards and power-washing is paramount. This requires camera enclosures that are rated for resistance to high-pressure water jets, dust and vandalism/tampering. Since edible processing requires stringent regulations be followed, it becomes more critical for security managers to identify solutions that carry the NSF Mark, making them compliant with standards set forth for commercial food equipment in North America, or the HCV EU, the equivalent in Europe. Many of these locations handle and store large amounts of cash since customers have to pay with cash Retail protection As the final products come out of processing and go into storefronts to be sold by retailers in States that have recreational or medical facilities, there's another level of security that must be in place to protect these transactions. But careful considerations must be made. Traditional security tags cannot generally be used because of the small size of many of the end products, making it more difficult to track with tracking devices.Traditional security tags cannot generally be used because of the small size of many of the end products In this instance, comprehensive video surveillance becomes the main tool for thwarting theft and addressing incidents as they arise. In these locations, a loss prevention or security officer has to be an integral part of the team. Another consideration is the careful screening of the potential employees. Since the federal government doesn't recognise cannabis producers and retailers, banks that are federally insured through the FDIC don't accept money from these establishments, meaning that many of these locations handle and store large amounts of cash since customers have to pay with cash. There must be security measures in place for these kinds of transactions, including the ability for video surveillance to be played back instantaneously in the event of an incident at a cash register. The cannabis market comes with a variety of challenges at each and every step of the operation, from growing to transport to production and sales. Video surveillance and business intelligence solutions are ideal for these applications, and as the market grows, more and more security companies will look to cater to the market.
Audio is often overlooked in the security and video surveillance industry. There are some intercom installations where audio plays a key role, but it’s not typically thought about when it comes to security and event management. Audio takes a back seat in many security systems because audio captured from a surveillance camera can have a different impact on the privacy of those being monitored. Audio surveillance is therefore subject to strict laws that vary from state to state. Many states require a clearly posted sign indicating audio recording is taking place in an area before a person enters. Analytic information derived from audio can be a useful tool and when implemented correctly, removes any concerns over privacy or legal compliance. Audio analytics on the edge overcomes legal challenges as it never passes audio outside of the camera Focused responses to events Audio analytics processed in the camera, has been a niche and specialised area for many installers and end users. This could be due to state laws governing audio recording, however, audio analytics on the edge overcomes legal challenges as it never passes audio outside of the camera Processing audio analytics in-camera provides excellent privacy since audio data is analysed internally with a set of algorithms that only compare and assess the audio content. Processing audio analytics on the edge also reduces latency compared with any system that needs to send the raw audio to an on-premises or cloud server for analysis. Audio analytics can quickly pinpoint zones that security staff should focus on, which can dramatically shorten response times to incidents. Audio-derived data also provides a secondary layer of verification that an event is taking place which can help prioritise responses from police and emergency personnel. Having a SoC allows a manufacturer to reserve space for specialised features, and for audio analytics, a database of reference sounds is needed for comparison Microphones and algorithms Many IP-based cameras have small microphones embedded in the housing while some have a jack for connecting external microphones to the camera. Microphones on indoor cameras work well since the housing allows for a small hole to permit sound waves to reach the microphone. Outdoor cameras that are IP66 certified against water and dust ingress will typically have less sensitivity since the microphone is not exposed. In cases like these, an outdoor microphone, strategically placed, can significantly improve outdoor analytic accuracy. There are several companies that make excellent directional microphones for outdoor use, some of which can also combat wind noise. Any high-quality external microphone should easily outperform a camera’s internal microphone in terms of analytic accuracy, so it is worth considering in areas where audio information gathering is deemed most important. In-built audio-video analytics Surveillance cameras with a dedicated SoC (System on Chip) have become available in recent years with in-built video and audio analytics that can detect and classify audio events and send alerts to staff and emergency for sounds such as gunshots, screams, glass breaks and explosions. Having a SoC allows a manufacturer to reserve space for specialised features. For audio analytics, a database of reference sounds is needed for comparison. The camera extracts the characteristics of the audio source collected using the camera's internal or externally connected microphone and calculates its likelihood based on the pre-defined database. If a match is found for a known sound, e.g., gunshot, explosion, glass break, or scream, an event is triggered, and the message is passed to the VMS. If a match is found for a known sound, e.g., gunshot, explosion, glass break, or scream, an event is triggered, and the message is passed to the VMS Configuring a camera for audio analytics Audio detectionThe first job of a well-configured camera or camera/mic pair is to detect sounds of interest while rejecting ancillary sounds and noise below a preset threshold. Each camera must be custom configured for its particular environment to detect audio levels which exceed a user-defined level. Since audio levels are typically greater in abnormal situations, any audio levels exceeding the baseline set levels are detected as being a potential security event. Operators can be notified of any abnormal situations via event signals allowing the operator to take suitable measures. Finding a baseline of background noise and setting an appropriate threshold level is the first step. Installers should be able to enable or disable the noise reduction function and view the results to validate the optimum configuration during setup Noise reductionA simple threshold level may not be adequate enough to reduce false alarms depending on the environment where a camera or microphone is installed. Noise reduction is a feature on cameras that can reduce background noise greater than 55dB-65dB for increased detection accuracy. Installers should be able to enable or disable the noise reduction function and view the results to validate the optimum configuration during setup. With noise reduction enabled, the system analyses the attenuated audio source. As such, the audio source classification performance may be hindered or generate errors, so it is important to use noise reduction technology sparingly. Audio source classificationIt’s important to supply the analytic algorithm with a good audio level and a high signal-to-noise ratio to reduce the chance of generating false alarms under normal circumstances. Installers should experiment with ideal placement for both video as well as audio. While a ceiling corner might seem an ideal location for a camera, it might also cause background audio noise to be artificially amplified. Many cameras provide a graph which visualises audio source levels to allow for the intuitive checking of noise cancellation and detection levels. Analytics take privacy concerns out of the equation and allow installers and end users to use camera audio responsibly Messages and eventsIt’s important to choose a VMS that has correctly integrated the camera’s API (application programming interface) in order to receive comprehensive audio analytic events that include the classification ID (explosion, glass break, gunshot, scream). A standard VMS that only supports generic alarms, may not be able to resolve all of the information. More advanced VMS solutions can identify different messages from the camera. Well configured audio analytics can deliver critical information about a security event, accelerating response times and providing timely details beyond video-only surveillance. Analytics take privacy concerns out of the equation and allow installers and end users to use camera audio responsibly. Hanwha Techwin's audio source classification technology, available in its X Series cameras, features three customisable settings for category, noise cancellation and detection level for optimum performance in a variety of installation environments.
If you’ve been paying attention over the last twelve months, you will have noticed that deep learning techniques and artificial intelligence (AI) are making waves in the physical security market, with manufacturers eagerly adopting these buzzwords at the industry's biggest trade shows. With all the hype, security professionals are curious to know what these terms really mean, and how these technologies can boost real-world security system performance. The growing number of applications of deep learning technology and AI in physical security is a clear indication that these are more than a passing fad. This review of some of our most comprehensive articles on these topics shows that AI is an all-pervasive trend that the physical security industry will do well to embrace quickly. Here, we examine the opportunities that artificial intelligence presents for smart security applications, and look back at how some of the leading security companies are adapting to respond to rapidly-changing expectations: What is deep learning technology? Machine Learning involves collecting large amounts of data related to a problem, training a model using this data and employing this model to process new data. Recently, there have been huge advances in a branch of Machine Learning called Deep Learning. This describes a family of algorithms based on neural networks. These algorithms are able to learn efficiently from example, and subsequently apply this learning to new data. Here, Zvika Ashani explains how deep learning technology can boost video surveillance systems. Relationship between deep learning and artificial intelligence With deep learning, you can show a computer many different images and it will "learn" to distinguish the differences. This is the "training" phase. After the neural network learns about the data, it can then use "inference" to interpret new data based on what it has learned. For example, if it has seen enough cats before, the system will know when a new image is a cat. In effect, the system “learns” by looking at lots of data to achieve artificial intelligence (AI). Larry Anderson explores how new computer hardware - the Graphic Processing Unit (GPU) – is making artificial intelligence accessible to the security industry. Improving surveillance efficiency and accuracy with AI Larry Anderson explains how the latest technologies from Neurala and Motorola will enable the addition of AI to existing products, changing an existing solution from a passive sensor to a device that is “active in its thinking.” The technology is already being added to existing Motorola body-worn-cameras to enable police officers to more efficiently search for objects or persons of interest. In surveillance applications, AI could eliminate the need for humans to do repetitive or boring work, such as look at hours of video footage. Intelligent security systems overcome smart city surveillance challenges AI technology is expected to answer the pressing industry questions of how to use Big Data effectively and make a return on the investment in expensive storage, while maintaining (or even lowering) human capital costs. However, until recently, these expectations have been limited by factors such as a limited ability to learn, and high ongoing costs. Zvika Ashani examines how these challenges are being met and overcome, making artificial intelligence the standard in Smart City surveillance deployments. Combining AI and robotics to enhance security operations With the abilities afforded by AI, robots can navigate any designated area autonomously to keep an eye out for suspicious behaviour or alert first responders to those who may need aid. This also means that fewer law enforcement and/or security personnel will have be pulled from surrounding areas. While drones still require a human operator to chart their flight paths, the evolution of artificial intelligence (AI) is increasing the capabilities of these machines to work autonomously, says Steve Reinharz. Future of artificial intelligence in the security industry Contributors to SourceSecurity.com have been eager to embrace artificial intelligence and its ability to make video analytics more accurate and effective. Manufacturers predicted that deep learning technology could provide unprecedented insight into human behaviour, allowing video systems to more accurately monitor and predict crime. They also noted how cloud-based systems hold an advantage for deep learning video analytics. All in all, manufacturers are hoping that AI will provide scalable solutions across a range of vertical markets.
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