Universities, colleges and schools are constantly challenged with how to ensure the safety and wellbeing of students and staff. According to Campus Safety, the majority of campuses believe video surveillance is the answer to their security needs. In fact, more than 9 in 10 campuses have installed surveillance cameras, and 79% of the campuses without cameras plan to install them in the next 3 years. On top of managing campus security, many educational institutions function as a mini-city alongside a profitable business, entailing various additional challenges. Not to underestimate the importance of security and its connection video surveillance cameras, but that’s a no brainer. The more intriguing questions would be: 1. Can the video from the campus cameras be used more effectively, enhancing security? 2. Can the same video from the cameras be used for additional applications beyond security? One solution addresses both questions: video analytics. To answer question #1, by using video analytics based on Video Synopsis®, campuses can review hours of video in just minutes, while applying sophisticated search filters to quickly find what they’re looking for. With the ability to review video rapidly, institutions can cover their entire campus – indoors and outdoors – with cameras, ensuring full coverage and control at all times. After all, what’s the point of a surveillance camera without an effective way to make use of what the camera is recording. The answer to question #2 requires keeping an open mind: the right video analytics solution can use the same video from campus cameras for other purposes than security. Without having to install additional software. How? The required video footage is processed only once and then manipulated per demand for various applications. When dealing with theft or attack on campus, the security department would review the relevant video and by applying Video Synopsis could achieve faster time-to-target with accurate investigation results, while managing future events proactively. Then, using the same video analytics software other stakeholders in the institute could generate statistical reports, enabling them to understand if and where there is a liability issue, such as a slippery path, lack of light, crowded exit, intruder, lack of privacy, etc. From a business perspective, this is where applying video analytics can be a real game changer: using the same video data to improve operations across campus, monitoring relevant activities, ensuring student satisfaction, gaining insights for future expansion of the campus, etc. Market experts believe that security technology for campuses will become even stronger than it was in 2016. And, with video analytics playing a key role, security is just the tip of the iceberg.
On the heels of the WannaCry ransomware crisis that had organizations worldwide reeling, we decided to take this opportunity to talk about how to protect your VMS and video analytics systems against cyber vulnerabilities. While the WannaCry attack did not affect IP cameras, VMS systems are not immune to security issues and it is important to be aware of how VMSs and the video analytics solutions integrated to them are affected by cyber threats. GenX security solutions has recently reported about the arrest of two London based individuals who were suspected of hacking into network video recorders in Washington D.C. just days before President Trump’s inauguration. Effectively, this hack disabled 123 of 187 network video cameras from recording, preventing video surveillance of most of the city until the recorders could be taken offline for the malicious software to be removed. Beyond these very practical threats to physical security, hacking into network based camera systems could offer back door access to wider corporate networks, posing even larger scale privacy and data security risks. To prevent these types of attacks, make sure to use a dedicated server for surveillance systems instead of putting company information on the same server you use for your VMS. Today, some forward-thinking video surveillance systems are built with cyberattacks in mind, and include additional functionality to protect against these threats. However, Security Today explains that, as the industry transitions from analog to IP cameras, not all camera manufacturers have built in protection against cyber threats. This could be because when IT safeguards are added after the actual installation of the video surveillance system, then the performance of the network could be irreparably damaged. Moving forward, VMS systems with IP cameras are likely to become more robust, with built-in protections against these types of attacks. But what does all this mean for your video analytics solution connected to the VMS? The most important thing to know is that, if you VMS has been hacked, the main practical application that might affect your video analytics activities is simply that, when your VMS stops recording video, your video analytics solution will not have video to analyze and process. Beyond that, any sophisticated video analytics system you use should: Provide you with password protected access and a limited IP address range, to prevent hackers from accessing sensitive information Implement its processing units to the VMS architecture as stand-alone components, so that even if a VMS lacks sufficient protection, risks to the video analytics platform are mitigated Protect you in all scenarios, so that even when the analytics processing software is based on video file handling and not VMS, you are fortified against attacks In today’s day and age, no one is 100% safe from cyber threats, but by taking the proper precautions and using intelligent video analytics platforms, you can keep your video data safe, even when you’re VMS has come under attack.
In my last blog post, I talked about the cost efficiency of GPU-powered video analytics, and how a major advantage of GPU processing is the enablement of deep learning techniques. In today’s post, I’d like to delve deeper into what deep learning is and what it enables video analytics solutions to achieve. Deep learning techniques use deep neural networks (DNNs) to train computer systems, imitating the way a human is taught and learns. Historically, deep learning has been possible since the 80s, but it took until now to really gain traction in video analytics because CPU-based processors were too slow for training neural networks effectively. Today, deep learning, running on GPUs, can be used for efficiently detecting, classifying and recognizing features and objects in video. These capabilities have transformed the video analytics industry by allowing security applications to work out-of-the-box on a broad spectrum of scenarios. Increased coverage and cost-efficient processing allows systems to continuously process more cameras and aggregate metadata over time, making video more accessible. This, in turn helps users to gain deeper insights from previously unused video. Beyond video analytics, deep learning techniques “are crucial to unleashing improvements in robotics, autonomous drones, and, of course, self-driving cars” (Source: Why Deep Learning is Suddenly Changing Your Life). Deep learning is a great development tool because it can complete many activities simultaneously. Multiple algorithms were once needed to compute different aspects of video analysis, but deep learning can solve many problems at once and, as it learns more, it becomes more equipped to solve more complex problems over time. The main challenge of deep learning is the large amount of annotated data required for effective training. The annotation process often involves labor intensive and repetitive manual work. It is often worthwhile to invest in annotation tools and in automatically generating annotation proposals. In addition, there is significant research in the field of unsupervised learning that will alleviate the need for manual annotation. Here are some other challenges to consider when adopting deep learning: While it’s beneficial for the system to solve problems independently, this means there is less visibility into how the problem was solved If the system isn’t exposed to a broad enough variety of data, it could reach wrong, often unexpected conclusions The GPU-processing needed to enable deep learning can be demanding and expensive to run The technology is rapidly evolving, so developers need to follow academic research and frequently re-assess their algorithms (agile) It is clear that the benefits of deep learning in video analytics, and many other fields, greatly surpass the challenges. It will be interesting to see how the technology develops as processing and automation technology improve. Perhaps in the future, systems will be so well-trained machines will be able to predict and interpret unfamiliar scenarios independently, and help provide further insight for improving security, business intelligence and quality of life. If you’re attending the GPU Technology Conference this week, you can learn more about Leveraging Deep Learning and GPUs to Accelerate Surveillance Video to Insight in a session with my colleague, Amit Gavish, BriefCam General Manager of Americas.
Anyone familiar with the ins and outs of hospital management understands that the security and operational challenges of hospitals are enormous. With thousands of patients, staff members, and visitors entering and circulating the hospital every day, there is much information to monitor and process. While many hospitals know that they need more effective methods for maintaining a safe and efficient environment, most hospitals lack the tools to manage their security and operational needs. One major setback many hospitals face is that they capture hours of video surveillance, but don’t have the resources to efficiently monitor and review the recordings. While this footage could be used to extract useful information and understand vulnerabilities and inefficiencies in the day-to-day hospital operations, petabytes of video data go to waste every day because there is no way of quickly and effectively reviewing all the footage. Video Synopsis® technology offers a smart and reliable video analytics solution for finding targets faster and enabling efficient monitoring. This makes it an ideal solution for maintaining hospital security and operations, facilitating hospitals to: Increase overall security Ensure patient and personnel safety Comply with procedures and regulations Mitigate false liability claims Improve operational management Optimize commercial operations The ability to easily manipulate video data with filters, makes it comfortable for anyone responsible for reviewing the data to find necessary information, detect anomalous behavior, and proactively prevent inefficiencies. We can see the profound impact of video analytics on hospitals by looking back at the Forbes interview with Director of Police, Security and Outside Services at Mass General Hospital, Bonnie Michelman, who anticipated how video analytics could influence hospitals, by combining video and analytics to identify risk situation, crowd control, unattended packages, abnormal traffic flow and suspicious loitering in restricted areas. Today, Mass General Hospital has implemented a video analytics strategy and is leveraging their video data to improve security and operations across the hospital. And it doesn’t stop at hospitals: As video analytics and Video Synopsis technology become more advanced, the types of organizations they can help will become more varied, leading to impactful results for many businesses and institutions.
ISC West 2017 was impressive, with many interesting products and new technologies – where GPU and deep learning took first place in prime time. Participating at NVIDIA’s booth and getting a first glimpse into exciting innovations, certainly highlighted the potential of video analytics powered by GPU and deep networks. With the ability to effectively process highly parallel computing tasks, such as video and graphics, today’s availability of GPU accelerates the process of deep learning, enabling real advancements, which until today could only be imagined. By applying GPU-based deep learning techniques to video analytics solutions, the market can expect faster processing of video alongside richer metadata. This combination will enhance the quality of object extraction and provide new applications that will be beneficial across many more verticals and use cases. Video analytics solutions, strengthened by deep learning, enable covering an entire scene for full object tracking at the highest level. Cost-efficient subscription-based cloud services for home and SMBs, trends over time, connecting to other applications, implementation with small appliances, cross camera search and family member identification are just some examples of possibilities the future holds. Furthermore, we can expect to see performance and accuracy improvements in: Video search, alerts and statistics Scene coverage for occupancy, crowd management and queue control Cross camera search and re-entry of objects Metadata aggregation over time and trending What’s clear from ISC West, is that expectations from deep learning are high – and rightfully so. GPU-based deep networks particularly facilitate making sense of actual video content. This means video can be searched by the content that is actually displayed in the video. Video remains the strongest sensor and metadata is still king. Leveraging deep learning capabilities, Video Synopsis® and analytics can deliver more value to various markets, from law enforcement and security to safe and smart cities, campuses and retail. Interested in learning more about GPU processing for video analytics? Check out CTO Tom Edlund’s latest blog post about cost efficient video processing using GPUs.
The video analytics industry is constantly producing new and innovative solutions to provide deeper insight and derive more useful applications from video. These new capabilities often require more compute power. GPUs (Graphic Processing Units), if used correctly, can accelerate video processing and reduce costs. Therefore, many platforms are now adding GPUs to offload the CPUs (Central Processing Units) processing. Recently, this trend has been accelerated by the wide adoption of Deep Neural Networks (DNNs) to analyze images and videos. GPUs were originally designed to optimize rendering-to-display devices and enabled the rapid advancement of the computer game industry. Today, GPUs are also used for General-purpose computing (GPGPU) using higher level frameworks such as OpenCL or CUDA. GPUs excel in repeated parallel computations of large data (aka Single Instruction Multiple Data, or SIMD) and are ideal for DNNs and many image processing algorithms. Also, GPUs have dedicated HW for video decoding and encoding. Considering these advantages, it may seem straightforward to run your video analytics on GPU and improve performance. This is true if most of your computations are done by large/deep DNNs or run full frame processing at high resolutions. However, video analytics are often highly optimized and limit the computations to small regions of interest and lower spatial and temporal resolutions, making it difficult to reach high utilization of the GPU. In such cases, you will need to invest more in your system architecture to fully utilize the GPU by using technologies such as batching and buffering. If you consider porting a video analytics engine from CPU to GPU you should ask yourself the following: Do I use DNNs for most of the computations today or am I planning to in the future? Are my computer vision algorithms GPU friendly (e.g. data size and parallel computations)? Where do I deploy my processing? Edge devices, laptops and workstations are ideal for GPUs, while its harder to reach cost effectiveness on local or cloud servers. Every organization’s video processing needs are unique, and the answers to these questions should help give you an idea as to whether moving to GPU processing might be worth considering for you. If you’re looking to gain deeper insight about running video processing on GPU, you are invited to join my talk on April 6th at 2:00pm at NVIDIA’s ISC West booth #20075.
Last week, I talked about how small and medium sized businesses (SMBs) face large challenges and whether they are getting value from their video recordings. This week, I want to ask the same question for homeowners. Are they getting their videos worth in the home? Ask anyone in the industry, and they’ll tell you that IP cameras are one of the fastest growing categories in consumer electronics, both as a stand-alone single point camera solution, or part of a monitored home security offering. SDM presents great perspectives on the role of IP cameras in the residential market in this article. The list of reasons why consumers buy a security camera is long and wide, but a common theme for the purchase is peace of mind. A security camera should be able to fulfill this promise because it allows people to: Know what’s going on in their home Gain a sense of security Be alerted when something goes wrong And, with mobile phones, check in from anywhere, anytime… But do the cameras really fulfill all of these promises? Unfortunately, I say they don’t…. If you watch almost any commercial for a home security camera, the focus of the marketing message is on the high level critical value propositions stated above, plus more. Things get complicated when the camera technology cannot fully deliver on either the promises made by the manufacturer, or the expectations of the consumer. Motion detection capabilities in a camera do exactly what they are supposed to do…. detect motion. Unfortunately, the camera does not have the ability to understand the relevance of the motion it’s detecting, so it sends an alert accordingly. It could be the family, the pets, trees blowing in the wind, light changes, etc. They detect and alert on ALL motion, not just the motion the owner wants to know about. Upon setting up a security camera in a home, most consumers are excited to get that first alert come across the phone indicating the camera detected motion. More often than not, Mom and Dad are telling the kids to go wave at the camera. Bzzzz! Your phone notifies you motion was detected. Hurrah!! Several days, or weeks later, after getting 20, 30, and sometimes 40 or more push notifications that your family and pets have walked in front of the camera, the homeowner gets frustrated, and ends up turning the alert capability off. I saw this exact consumer behavior pattern in my prior platform, and have heard the same from other platform providers. Once notifications are disabled, one of the very core reasons a consumer bought the camera to begin with goes away, and the expected value is no longer there. Rather than detect only motion, what if the camera could detect people, animals, vehicles, or more importantly, trusted family members? What if you only got alerts when the dog got on the couch, but not when your kids walked through the living room? What if you got an alert when a burglar was actually in your home rather than a trusted family member? What if you could see a 1 minute video summary of all of the activity in the home, and if you saw something of interest, you could stop and go to the original video from earlier in the day. These capabilities alone would fulfill the implied benefits and expected value consumers expect from a camera.
Small and medium businesses (SMBs) are faced with large challenges year after year. The three main business challenges for SMBs in 2017 are the same as in 2016, but in different order. Improve workforce productivity (2nd in 2016) Attract and retain new customers (3rd in 2016) Improve quality of products and processes (1st in 2016) On top of these 3 challenges, there is always the matter of security. Call it loss prevention, theft, shoplifting or retail shrinkage, this continually burdens all types of SMB owners. Additionally, statistics indicate every 1 in 11 people is a shoplifter, which is a huge concern for any business owner. Usually different problems require different solutions. However, with video analytics capabilities today, there’s one solution to solve all those concerns. Video data. By making use of video the right way, SMBs can manage business challenges and security issues easily and efficiently. As we’ve mentioned before, the #1 sensor is video, and for SMBs this means getting valuable information, which no other standard sensor can provide. Any business owner knows that the more information they have (providing they can actually make sense of it), the better value they can create for customers, and the more effectively they can manage their business. With the proliferation of surveillance cameras, more and more video data is available for SMBs. The problem however, is that typically, the video from surveillance cameras is either never viewed, or only viewed close to the time of an incident. And in general, the information generated from the video is never realized, data is forgotten, archived (which really means never to be found again), or simply deleted. If video essentially means data, i.e., valuable information, then why would any business owner ignore it? SMBs – read my lips: You can increase sales and profit by using video from your already-installed cameras. You can ensure a secure business by quickly applying Video Synopsis. You can gain insights from video data to run your business more efficiently. You can better understand customer behavior, why they do or don’t purchase. You can monitor employee productivity. You can understand optimized product layout. You can count how many customers walk into your business and see what they do. You can optimize your marketing efforts. And finally, you can sleep better. Next week, we'll take a look at whether homeowners are getting their video's worth from the cameras they install in their homes.
I have heard from many investigators that the most painful part of their jobs is reviewing video. Often investigators are working with too little information and viewing too much video to be effective in finding anything. Reviewing video no longer has to be an experience that leads to you tearing out your hair. It does not need to be like searching for a needle in a haystack, a futile waste of time. So what wastes time reviewing video? Looking at things that are not what you’re looking to find. I hope you weren’t expecting a more sophisticated answer, because there is none. It’s as simple as that. If you have to go through a large amount of video, it can help immensely to review a subset of the relevant things in that video. Less video data simply means less review time. So how can you reduce the video you must review to find your target? This is achieved by eliminating objects that are not relevant to what you want to find, while still keeping the objects relevant to your search. This is a simple concept, but takes many years of effort and significant technology to do well. Are there other means of reducing the video you need to review? Motion search is clearly not the solution for eliminating what you don’t want to see. While this may slightly reduce the time of reviewing video, this is in effect putting a bandage on an open wound since it still overwhelms an investigator with a long series of individual events for review. Effective review requires more capable tools. This is where the ability to add layering of analytical filter parameters (such as size, direction, color, path, speed), yields reduction in the number of results for review, leading to shorter review time. So really it’s what you don’t want to see that has to be taken into account when reviewing and searching video. This of course varies tremendously based on the person, vehicle or object you’re looking to find, the nature of the investigation or situation, and what you know about the suspect or event. That’s why it’s critical to leave the choice of eliminating objects in the hands of whoever is viewing the video and provide that person with tools that enable quick and interactive work with the search results. No matter how you slice it, if you can use what you know to reduce the results, you can cut the time you need to spend reviewing video. This helps find what you’re looking for, and in less time.
I spent that past couple of weeks at the leading VMS players’ annual events for partners, integrators and end users. Much was said and discussed on the increasing need for reliable video analytics solutions, and finally, thanks to today’s video quality and management capabilities video content can be leveraged for the benefit of all types of verticals and use cases. From security and safety, to retail, operations, business insights and even quality-of-life, everyone agreed that great value can be derived from video. It is, after all, considered to be the richest sensor. Still, what I really took away from these events was the inspiring notion that the promise of video analytics is so big; and we have so much more to discover and benefit from. Essentially for most people, video is video, is video. However, when we take a deeper look inside the video, we see that we're only scratching the surface by looking at what video presents to us overtly. To gain more from video, in ways we could only have imagined in the past, video should be used for its metadata (information about video data). Metadata provides a whole new meaning to searching, tracking and understanding object behavior within a video. In fact, you can even learn and monitor human behavior by using metadata. Metadata also enables video analytical capabilities in complex and high-activity scenes. Finally, with metadata, all types of details can be organized, whether counting cars crossing a junction or seeing where shoppers dwell in a store. With the right tools, valuable insights can be gained through metadata. Yes, metadata is king. As a result of the significant advancements in video analytics, metadata today presents information with very rich and detailed content – something organizations of all types want to hold on to. Furthermore, metadata is easier to store and save than actual video files. Metadata takes up less bandwidth, thus, extracting information from the video in the form of metadata becomes quick and simple. The metadata can then be cost-effectively uploaded to the cloud and aggregated for generating reports and identifying trends over time. Essentially, metadata is the key for further unlocking the potential of video analytics. Cutting-edge technologies such as deep learning, coupled with the power of metadata, ensure that video analytics continues to intrigue the industry and present new and exciting capacities, enabling more and more verticals to benefit from the promise of video analytics. All we need to remember is to take a better look into the video and not be fooled by what we simply see. Sometimes it’s what we don’t see at first that really matters.