AXCESS CCTV Network / IP Cameras

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. 

The Internet of Things (IoT) is having a significant and ever-changing impact on the way we view video security. Today, cameras are expected to be so much more than devices with which to simply capture images; they need to be far smarter than that. These future-facing cameras are becoming an integral part of the vast digital connectivity infrastructure, delivering a parallel performance as intelligent sensors with the ability to extract the kind of invaluable data that helps businesses make improvements in the area of video security, and beyond. However, as the list of possibilities grows, so too does the risk of unauthorised access by cybercriminals. We should all be aware that a single weak link in a communications infrastructure can give hackers access to sensitive data. That’s the bad news. Safeguarding data and utilising deep learning The good news is cybercrime can be avoided by employing a data security system that’s completely effective from end-to-end. One technological advancement that the trend-spotters are predicting will become part of the video security vocabulary is ‘deep learning’ Once this level of safeguarding is in place you can begin to confidently explore the technologies and trends happening now, and those on the horizon. So, what will be having an influence on surveillance in 2018? Well, according to IHS Markit, one technological advancement that the trend-spotters are predicting will become part of the video security vocabulary is ‘deep learning’, which uses algorithms to produce multiple layers of information from the same piece of data, therefore emulating the way the human brain absorbs innumerable details every second. In Europe, GDPR compliance will also be a big talking point as new principles for video surveillance data collection, use limitation, security safeguards, individual participation and accountability are introduced. And, as the popularity – and misuse – of drones continues to rise, the recent developments in drone detection technology will be particularly welcomed by those whose primary concern relates to large areas, such as airport perimeter security. The future of 'smart' video analytics An important feature of today’s intelligent cameras is the ability to provide smart video analytics. The Bosch ‘i’ series, for example, offers a choice of formats – Essential Video Analytics and Intelligent Video Analytics. Essential Video Analytics is geared toward regular applications such as small and medium businesses looking to support business intelligence (e.g. inter-network data transfer), large retail stores and commercial buildings for advanced intrusion detection, enforcing health and safety regulations (no-parking zones or detecting blocked emergency exits) and analysing consumer behaviour. The camera-based, real-time processing can also be used to detect discarded objects, issue loitering alarms and detect people or objects entering a pre-defined field. Intelligent Video Analytics provides additional capabilities. It is designed for demanding environments and mission-critical applications, such as the perimeter protection of airports, critical infrastructures and government buildings, border patrol, ship-tracking and traffic-monitoring (e.g. wrong-way detection, traffic-counts and monitoring roadsides for parked cars: all vital video security solutions). An important feature of today’s intelligent cameras is the ability to provide smart video analytics Intelligent Video Analytics can also differentiate between genuine security events and known false triggers, such as challenging environments created by snow, wind (moving trees), rain, hail, and water reflections. For more expansive areas, like an airport perimeter fence, the system has the range and capability to provide analysis over large distances. And, if a moving camera is employed, it is also possible to capture data on objects in transit when used in conjunction with the Intelligent Tracking feature. For roadside use, Intelligent Video Analytics systems, such as the Bosch MIC IP range, are resistant to vibrations and can still operate in extreme weather conditions, continuing to detect objects in heavy rain or snow.  Evolving cameras past surveillance It’s becoming ever clearer that the IoT is transforming the security camera from a device that simply captures images, into an intelligent sensor that plays an integral role in gathering the kind of vital business data that can be used to improve commercial operations in areas beyond security. For example, cities are transitioning into smart cities. The capabilities of an intelligent camera extend to the interaction and sharing of information with other devices (only those you have appointed) With intelligent video security cameras at the core of an urban infrastructure smart data can be collected to optimise energy consumption via smart city lighting that responds to crowd detection and movement. Cameras can also be used to improve public transport by monitoring punctuality and traffic flow based on queue lengths, with the ability to control traffic lights an option should a situation require it. As the urban sprawl continues and this infrastructure grows, the need for more knowledge of its use becomes more essential, necessitating the monitoring technology developed for use by human operators to evolve into smart sensing technology, that no longer just provides video feeds, but also uses intelligent analytics and sophisticated support systems. These systems filter out irrelevant sensor data and present only meaningful events, complete with all relevant contextual data to operators to aid their decision-making. Expanding the video security camera network Today, video analytics technology has tangible benefits for human operator surveillance, and delivers KPIs that are highly relevant to transport operators, planners and city authorities. As an existing infrastructure, a video security camera network can be improved and expanded by installing additional applications rather than replaced. From a business perspective, that means greater value from a limited investment. Thereafter, the capabilities of an intelligent camera extend to the interaction and sharing of information with other devices (only those you have appointed), image and data interpretation, and the ability to perform a variety of tasks independently to optimise both your safety and business requirements. The fact is, cameras see more than sensors. Sounds obvious, but a conventional sensor will only trigger an alarm when movement is detected, whereas a camera can also provide the associated image and information like object direction, size, colour, speed or type, and use time stamps to provide historical information regarding a specific location or event. Based on this evidence, the video security camera of today is more than ready for the challenges of tomorrow.