Prakash Prabhu – Chief Business Officer & Co-Founder, VisionBot
In 2022 the world would have surpassed 1 billion video cameras in use. While the number is huge, the future is staggering. Today there is 1 camera for every 8 people. It is projected that by 2030 there will be 13 billion video cameras in use. In just 8 years we will move from 1 camera for every 8 people to 1.5 cameras for each person. We can aptly call it the World Vision Web.
How will technology cope with such a humongous growth?
CLOUD based CCTV Video surveillance can be the bridge that will allow us to cross this challenge
The capabilities of CCTV video surveillance systems are now being transformed by fundamental shifts in how visual content inspection and monitoring data is gathered and analysed for actionable insights. This has profound implications not just for the effectiveness of CCTV video surveillance as a security tool but deployment of Video Content Analysis (VCA), for a range of other, non-security applications. There is clearly a requirement for newer ways these systems can provide organisations with more tangible returns on their CCTV video surveillance investment than ever before:
- Risk & compliance management.
- Unified threat management.
- Loss prevention/ detection systems.
- Business continuity/ reporting.
- Bulk video synopsis.
- Information and event management.
- Identity and safety management.
Impact of AI & ML on the Enterprise Security and Visual Monitoring and Inspection market
Traditional rule based VCA adoption is on the rise and more providers are incorporating it in their cameras (Edge) and Video Management Systems. But it is basically the mechanism to incorporate intelligence into Computer Vision devices through a pre-determined set of rules (algorithm). This may satisfy the needs of security and investigations, but are found lacking for the demanding visual content inspection and monitoring requirements.
When it comes to buzzwords, Artificial Intelligence (AI) has a higher recollect and is much used in the analytics domain than the more specific related terms machine learning and deep learning which are a better description of cognitive tools that are being deployed for computer vision.
Machine Learning: Machine Learning, a sub set of Artificial Intelligence (AI), uses statistical methods and is basically the process which provides the system (computer) to learn automatically and improve accordingly without being explicitly programmed. We can classify ML as Supervised Learning, Unsupervised Learning and Reinforcement Learning
Deep Learning: Deep Learning is basically a sub-part of the broader family of Machine Learning which makes use of Neural Networks with a large number of parameters layers classified as Convolutional Neural Networks, Recurrent Neural Networks and Recursive Neural Networks. DL works on larger sets of data when compared to ML and prediction mechanism is self-administered by machines.
Deep learning systems can continuously calibrate vectors assigned to various inputs to better understand their environment. While standard systems tend to scrutinise pixel values for input data, deep learning systems can also use temporal, spatial and other visual elements to recognise and classify objects and events. Deep learning algorithms can handle larger datasets, including unlabelled data, in less time than traditional algorithms.
Important reason for AI/ ML adoption will be more accuracy, reduced costs, improved productivity while requiring minimal human intervention to adapt and teach systems, which the AI will do itself as it continually learns. Deep learning algorithms, which continuously self-optimise based on analysis of data gathered, has supercharged VCA.
It will be the next best transformation for the Visual Content Inspection and Monitoring industry after adoption of Network video for CCTV Surveillance in the early 2008.
Industry experts agree that the mentioned environments will benefit most from AI/ ML deployments:
- Crowded places (public squares, shopping malls, sports stadia etc).
- Law enforcement.
- Smart cities & infrastructure.
- Retail banking/ finance
- Transportation & warehousing.
- Utilities, energy, oil & gas.
- Professional sports.
- Media & broadcasting.
Visionbot™ – Enabling the Convergence of Cloud and AI/ ML
While technology itself is evolving, computing power doubling, challenges do remain for the customer in deployment of AI/ ML. They are cost/ scalability/ technological obsolescence and adaptability to evolving organizational needs. Absence of an EXIT option once the organizational priority has moved on is also a bottleneck in customer adoption.
Cloud Based AI/ ML platform is an ideal solution for enterprises to onboard their transformational AI/ ML journey for visual monitoring and inspection needs
Visionbot™ a brand of Amvar Datatech Pvt Ltd, an IT company focussed on computer software design for Machine Learning, computer software consultancy for artificial intelligence, Software as a Service for machine learning, Software as a Service for surveillance and other related services.
Visionbot™ uses a patented and award winning innovative Open Technology Platform which utilizes modern AI/ DL technologies of computer vision and natural language processing to help derive objective insights and reporting from subjective visuals captured over realtime camera video, recorded camera images and streaming broadcast feeds.
Visionbot™ was conceptualized as an adaptive self-service platform to leverage Machine Learning and Deep Learning for Computer Vision to provide ‘Objective Data from Subjective Visuals.’
The platform offers organizations to get specific insights of their visual content helping derive powerful insights and driving decision making. Designed as a cloud-based Software as a Service (SaaS) model and also with an on-premise option, Visionbot™ lets users start benefitting from the system with minimal investment.
Mission: Democratizing Video AI for ALL
To be the accelerator in bringing customers the full benefits of their intelligent network video investments. Deliver a compelling convergence of adaptability, agility and affordability that dramatically simplify the deployment and acquisition of AI data with increased agility, sensible economics and a seamless end-user experience.
Addressing The Problem
Among all the workflow processes within any enterprise today – visual inspection and monitoring is still heavily dependent on human reporting. Human assessment and reporting are however subjective and is prone to errors due to fatigue and inconsistency/ non repeatability. It also slows down the overall process throughput which are otherwise completely automated. Visionbot™ assists companies to reduce operational costs and mitigate safety and environmental risks through intelligent visual monitoring. With our platform clients are enabled to increase productivity through virtual asset inspections, improve HSE, and manage customizable outcomes with monitoring, automated reporting, and strengthen security through predictive activity detection and alerting.
Visionbot™ delivers smarter adaptive Cloud based video AI solutions that provide maximum resource utilization, deliver high availability & performance accuracy for the applications that matter most. It provides a pre-trained machine learning model that can automatically identify a large number of objects, locations, and actions in stored and streamed videos. It works out-of-the-box, offers high performance in common use cases, and is constantly being updated with new objects and concepts. AutoML Video Intelligence provides a graphical interface, allowing users with minimal machine learning experience to train custom models, in order to classify and track objects and events in a video.
Cut routine site visits. Saves time and labour costs via online inspections and alerts.
Prioritize activity. Gets automated notifications of activities when and where you want alerts, frequency of alerts and respond quickly to problems.
Improve productivity. Empower your operators to focus on productive and higher-value work. Increase profitability. Decrease operating costs and improve production uptime.
Reduce Costs. Visual validation of WIP, decrease losses and ensuring greater accountability/ procedural compliance.
Increase security and activity awareness. Understand the activity that occurs and both manned & unmanned assets while increasing security with activity alerts.
VisionBot Partner Program
We believe in building strong industry partnerships to address the growing organizational demands for real time accurate data. Our cloud based, scalable SaaS platform will be a disruptor in the video AI analytics space and brings the value addition many service and infrastructure providers would like to provide to their customers. It also offers customers the fastest deployment cycle and immense scalability on the cloud.
As an agile start-up we bring a strong commitment to our partner relationships. Some of the ways you could choose to partner with us are as follows. Reach out to us to discuss how we can together assist customers in their AI/ ML journey.
- Video Integration Partners: Companies Currently involved in delivering End to End Security and Surveillance Solutions.
- Alliance Partners: Companies involved in providing SaaS services for IT and Video hosting.
- Solution Development Partners: Companies and Consultants who would like to build service offerings viz: Business Reporting, Process Improvements etc, using information from the VisionBot Platform.