Your surveillance system might be the chink in your armour. Make it fool-proof with AIVI

Dozens of digital eyes peer at us today – guarding us from the moment we step out of our homes, and sometimes even before. We have surveillance cameras watching over our gates, our roads, and our offices, and even overseeing us in shops, malls, and ATMs.

Between Chennai, Hyderabad, and Delhi being among the top ten cities in the world with the highest number of CCTV cameras per square kilometre, and video surveillance buttressing Indian investigative agencies as they crack violent cases – these cameras have already proven themselves to be indispensable. It is, therefore, imperative to have cameras surveying your company’s premises, and to use them as the bedrock of your business’ security structure.

But these digital eyes are only as good as their human counterparts monitoring a feed. CCTVs’ watchful presence might prove to be a powerful deterrent, but their passivity results in complete reliance on external forces for intervention. These days, law enforcement too depends on captured footage to solve crime.

Things are changing, however, with AIVI (Artificial Intelligence Vision) – the tool that effectively marries computer vision algorithms with data-driven learning of Artificial Intelligence. Installing AIVI technology amplifies a company’s video surveillance system, transforming billions of hours of footage from an overwhelming wave of monotony into an organized database to apprehend perpetrators.

Here are six ways in which computer vision can help your company reduce security risks:


Cameras with AIVI are significantly more adept at recognising and labelling objects – whether human, vehicle, or weapon. Installing this technology is an easy investment, especially considering most facilities are equipped with CCTV cameras.

This intelligence elevates an average camera, making it accurately identify a human presence even in the case of a partial capture. This process can even be widened to study people’s proportions and gaits, and can be trained to identify people based on these traits – even during less than ideal circumstances.

Facial recognition

The specificity that computer vision-powered cameras allow for, based in the technology’s pattern-recognition techniques, enables them to verify the identity of individuals based on extremely distinct features – like biometric identification through iris and retina scans. By extension, this technology allows for digitising all company documents since signatories can be corroborated through this highly precise process. These modes of biometric authorisation simplify and solidify security processes, and consequently reduce the scope for fraudulent activities.

Future forward

With the possibility of analysing movements and recognizing suspicious behavior in real time, and immediately notify authorities. Something as minute as a deceitful glance can be captured. From ringing alarms exactly when an item is shoplifted, to alerting authorities, vision intelligence turns CCTV cameras into a proactive part of cracking crime. Computer vision can also identify contextually dangerous behavior – such as a worker showing signs of fatigue and dozing off while operating high-risk equipment.

Outside of security concerns, these AIVI capabilities can track behavior to provide detailed insights into what stakeholders and business partners respond to positively and negatively on company premises. Just tap into the data AIVI gathers on your organisation’s performance indicators – from customer satisfaction to client retention rates.

Omnipresence: Computer vision can monitor all visitors on the premises, and alert security teams in a fraction of a second if an attempted robbery or security breach is occurring. The response – such as locking down the area – can be immediate. This promptness, along with hyper-specific discernment, enables security teams to share access privileges on an individual level, and allows them to even track miscreants through vision intelligence.

Furthermore, critical assets can be surveilled in real time – whether they are highly valued technologies or cash. AIVI’s collection of historical data and pattern-learning capabilities can even offer a guide to where human resources need to be focused to strengthen the company’s security structure. And in case the crime has already occurred, security personnel can rely on the database to sift out relevant information within seconds – whether that detail is a man in a blue shirt, or a license plate. Computer vision – through a technique called “generative adversarial networks” – even offers the ability to enhance and recreate images, providing security teams more valuable visual details around critical incidents.

Safety measures: The midst of a raging pandemic, AIVI-enabled cameras can ensure that employees and workers socially distance on the premises. This technology can also alert workers who are dangerously close to hazardous equipment, or toxic substances.

Computer vision’s deep learning faculties can also understand manufacturing and standard operating procedures, and make sure materials and processes are being handled safely on the factory floor.

Similarly, AIVI can track statutory compliance and make sure the company adheres to rules and regulations including the Factories Act, Shops and Establishment Act, and even Sexual Harassment of Women at Workplace (Prevention, Prohibition, and Redressal) Act.

Quick training: Newly hired employees can receive access privileges based on their scope of work. AIVI’s expanding database can also serve as an exhaustive resource when it comes to training workers on standard operating procedures, and areas that need the focus of increased human resources – for instance, deploying more security personnel at risk-points.

Computer vision capabilities don’t replace human resources, rather they enhance and augment manned security systems. AIVI’s surveillance offerings can predict critical security instances before they occur, prevent them from happening through a real time warning system, and protect you and your company’s valuable assets. This is an irrefutably reliable way to make your security systems proactive and fortified.

Roadmap for Vision Intelligence and why more industries will embrace Vision AI

Over the past ten years, Artificial Intelligence or AI technology has hurtled towards unimaginable advancements. And even though most people remain unaware of what AI tech such as Computer Vision (CV) or Vision Intelligence entail, chances are that they’ve already used it. AI has ubiquitously entered all our lives; it is helping us to drive smarter, unlock our phones faster, shop better, and soon, it will be a part of almost every aspect of our lives.

What is CV or Vision Intelligence and why will we see more of it in the years to come?

As human beings, we have the amazing ability to sense our surroundings. With the help of our eyesight and cognitive capabilities we can visualize what is around us and make decisions based on what we see. Computers on the other hand, aren’t able to do this automatically. CV or Vision Intelligence is thus a subset of AI that enables computers to see, identify, and interpret visual data as humans would. The process is complex and requires vision algorithms and applications for the computer to learn. However, once the process is complete, computers can see, interpret, and analyse visual data much better and faster than any human ever could. In addition to being more efficient, Vision Intelligence is also an extremely malleable technology. From automobiles to agriculture, Vision AI can be tailored to meet the requirements of all sorts of industries and its uses are wide-ranging. Below are examples of how Vision AI is being customised to help a whole host of industries.


Vision Intelligence has been a revolutionizing force in the manufacturing space. From smart factory floors to quality control and accident prevention, Vision AI can help with almost every manufacturing process. In a modern factory setup, automated production lines are fitted with multiple moving machines such as conveyor belts and robotics units. For seamless production to continue, none of these systems can afford a breakdown. However, more often than not, stoppages do happen and they hamper production. Here is where Vision AI steps in. Armed with AI-based vision, CCTV cameras can analyse and diagnose every minor defect in a production line and issue real-time updates in case of machine failure or other problems. For example, if a conveyor belt is stuck due to improper material alignment, CV will preemptively flag the issue and notify the shift manager. Or, if a worker is standing too close to a vat of dangerous chemicals, AI-based CV systems can issue a red alert, thereby avoiding an accident. These are just a few examples of how AI has been a game-changer in manufacturing.


Retail is another sphere in which Vision Intelligence is creating ripples. For too long now, retail stores and supermarkets have faced a host of issues such as inventory mismanagement, revenue loss, and theft. Vision Intelligence has a solution for all of this. With the help of CCTV systems, and cameras placed on shelves and other crucial points, images of products and customers are captured, processed, and analysed to help retailers draw actionable insights. Vision-based tech and Deep Learning algorithms thus help generate insights like the effect of product placement on sales and customer shopping patterns in order to create more effective and personalised shopping experiences. AI Vision-powered cameras can also help to detect theft or incidents of sweethearting which is a form of theft where cashiers or checkout counter employees can give away merchandise to a “sweetheart” customer such as a family member or friend.


In healthcare, Vision AI has the potential to save lives. Technologies like Automated Pathogen Detection combine the power of AI and automation to help test samples of human tissue, sputum etc. in a faster and more accurate manner. Meanwhile, there are several other AI-based tools that are being developed to analyse three-dimensional radiological images – a process that could potentially speed up diagnoses and suggest much-more effective treatments for patients.

The above three industries are just a few of the examples out of a vast pool of sectors that Vision Intelligence is making a splash in. In the years to come, Vision Intelligence or Computer Vision will grow in its reach and capabilities, and more and more industries will realise its multifaceted potential.

Vision Intelligence could be a permanent feature in the post-pandemic retail store

We are a year into the novel coronavirus pandemic and it is now evident that shopping will never be the same experience again. Beneath its glitzy exterior, shopping at its very heart is a collective experience; it is a way for us to socially interact with friends, family or even strangers. And even though the pandemic has robbed us of this banal pleasure and pushed consumers towards online shopping, brick and mortar stores are here to stay, in fact, according to Euromonitor International – an English strategic market research firm – 83% of all products purchased globally in 2022 will still be bought in-store. However, social distancing is also a reality that isn’t going away anytime soon and customers are going to want safe shopping experiences. It is here that Artificial Intelligence (AI) holds vital answers and solutions.

Vision AI in Retail

Today, digital transformation in retail is all about connecting technologies and converting data into valuable insights so as to improve customer safety and experience. A distinct subset of AI’s Deep Learning capabilities known as Computer Vision or Vision Intelligence is leading the way in this sphere.

Vision-enabled AI is a relatively new field of computer science that integrates Video data, Computer Vision and Machine learning, and trains computers to replicate activities and identify patterns. Vision Intelligence (sometimes rudimentarily referred to as Video Analytics) works by interacting with the surroundings, helping the computer ‘sense’ and ‘recognize’ the live environment and ‘learn’ from the memory of past experiences by extracting patterns in visual signals. It provides the best contextual information to help one speed up business operations since it literally turns camera images into actionable insights and helps retailers be situation-aware real time.

During the pandemic, Vision Intelligence has opened the door for retailers to have their brick and mortar stores fully compliant with Covid-19 safety rules and regulations.

Safety guidelines adherence 

AI-trained CCTV video analytics can be used to ensure social distancing is maintained. Essentially, ceiling-mounted cameras armed with computer vision use Deep Learning AI models to analyse videos and issue real-time alerts about people gathering in large groups or customers not wearing masks, etc. Vision Intelligence can also help store managers allocate personnel based on an analysis of customer inflow patterns; vis-a-vis footfalls, buying patterns, etc. Additionally, features such as heat maps automatically check temperatures of all customers, while issuing real-time alerts in case of anomalies.

Contactless shopping

Shopping in a post-pandemic world is going to focus on contactless technology. Anxious consumers worried about contracting pathogens from a point of sales (POS) will want to view and buy products with little to no physical contact. With the help of augmented reality apps, consumers can see display renderings of products and ingredients while maintaining social distancing. And no-contact checkout technology that uses AI tech can automatically detect when products are taken from or returned to a shelf, thus keeping track of a virtual cart. A contactless purchase means that a customer can simply exit a store, receive an e-receipt, and pay online. This reduces close-contact interaction and helps store staff to focus on other aspects of customer service and management.

In addition to compliance, Vision AI can additionally help retailers gain better insights into customer preferences and create more personalized shopping experiences. Vision AI studies consumer interactions and non-intrusively collects data that will help in determining, developing and implementing tailor-made solutions. For example, features like dwell time monitoring and facial emotion tracking, it is possible to address customer frustrations in real time by eliminating slow billing, predicting queue build up, identifying and assessing inappropriate staff behaviour, and resolving inadequate staff availability in certain areas. Advanced pattern recognition algorithms are also now available to help tracking aisle behaviour of the shopper, which also helps to understand shopper interests, tastes and preferences, as well as improve product placement.

All over the world, whether it is small boutiques or multinational supermarkets, retailers are looking to create convenient, personalised, enjoyable, and most importantly safe shopping experiences. The need for socially distanced shopping has accelerated the need for stores to adapt to digitisation. Computer Vision or Vision Intelligence seamlessly offers retailers the chance to improve their business while adhering to global safety guidelines in a post-pandemic world.

An introduction by Cogniphi VP, Seshagiri Sriram, to Dockers and how it simplifies a developer’s life

This is the first in a series of articles on docker. In this article, we aim to introduce the reader to the concepts of containers and some of the benefits they offer.

Sub-sequent articles will focus on deep dive into the usage and technologies underlying Docker and its eco-system. 

What is Docker?

Docker is a platform that uses containerization technology. It is used to package an application and all its dependencies together inside containers.  By doing so, the application works in any environment (at least in theory) – One way to look at it is to think of it as an easy way to ship “production ready” applications with all dependencies packed in.


A virtual Machine (VM) is a file. This file is called an image. However, the file has an interesting property – with the right tools, the file acts as if it was an actual physical computer. What this means is that you can have multiple virtual machines running inside one physical machine – this saves cost of provisioning a physical computer and associated costs – licenses, maintenance etc.

A VM can then be thought of like a special program – a program that runs an operating system or part of it. A VM is said to be sandboxed – tech geek speak for saying that it is isolated from the host operating system. This means that it can be used for several uses including but not limited to

  1. Testing Other operating systems (including Beta Releases)
  2. Accessing data that cannot be accessed normally (typically virus infected data),
  3. Performing OS level backups
  4. Running programs that were not meant for the host Operating system itself.

More than one VM can be run simultaneously on the same Physical computer. This is done by means of a special software called a hypervisor. Each VM provides a set of virtual hardware – basically each VM shares resources with others saving costs in physical hardware and associated maintenance costs – people, power, cooling – among others.

Figure 1: Core Virtualization
Fig 1.a – MS-DOS (Yes the old version running on Windows 10 Machine)
Fig 1-b – Windows 3.11 (yes old version of Windows on Windows 10)

So Why do we need a Container?

Running multiple virtual machines on same machine take a long time to boot up. In addition, these may cause performance issues. Management is another issue that is not simple when running multiple VMs.

If you have seen figure 1-a and Figure 1-b and think it’s easy (well actually it is ?), setup is really messy and very non-intuitive – setup time takes a long time and some complicated process to follow to get it working right.

Think of a Container as a virtualization at the OS level.

Figure 2: Containers

Some of the advantage of containers over VMs are:

– These tend to be more light weight,

– boot up faster,

– can be managed better (auto removal when done for example).

These advantages are shown graphically below:


– Containers allow you to run more applications on a physical machine than VMs. When resources are a constraint, containers may be a better choice.

– Containers allow on to create portable and consistent operating environments – Development, Staging, Production. This consistency helps in reducing development and deployment cost, besides making it easier to monitor systems to ensure higher level of availability to the end customer.

Docker Terms and Terminologies

Docker Image

A docker image is a read only template used to create container. THIS IS IMPORTANT – READ ONLY TEMPLATE.  These images are either built by you, or readily available from Docker Hub or any other repository.

Docker Container

A Docker container is an instantiation of one or more image. It contains everything that is needed to run the application – from the OS to the network to libraries to the actual app. This is the actual “running” instance.

Docker Daemon

The docker daemon is the core of Docker. It works together with the Docker CLI (command Line Interface). Think of it as a service that runs on the host Operating System (and yes, it runs now natively on Windows 10 upwards – Older Versions of Windows runs a stripped down version called Docker Desktop).

Docker Architecture – a 10000 feet view


We have the client (usually the Docker CLI). Note that since the Daemon has a REST API, you could write your own clients in Java or C# to call the Docker Daemon.

The docker host is the server where the Docker Daemon is running. The client and host do not have to be on same machine – that’s the key takeaway.

Finally, we have the registry where the images are stored. (and in Cogniphi, we have our own registry hosted in Azure, but it could very well be a standard image pulled from Docker Hub. And for those technically inclined, the registry is an actual docker container itself).

So what happens when you issue a command like below (In next articles, we will discuss all the commands and the meaning of various parameters)

docker run ubuntu /bin/bash

The client makes a request for an image called ubuntu. If one is found, well and good. If not, it pulls down the image from the registry, Next it starts a container from the image and runs /bin/bash. When the command finishes execution, the container is automatically stopped.

Why is this so important?

When you want to go to market quickly, you do not want to spend a lot of time setting up infrastructure. Docker comes with a large number of pre-built images – that you can deploy in a matter of minutes.

For example, you want to set up Apache HTTPD. On a windows world, you would need to first find the relevant Apache HTTPD Binary, download/unzip it, configure it, run it – a set of activities that can easily take up to 1 day. With docker it becomes a single liner

docker run httpd

While the above example is over simplified, it still can be started in a few minutes not days. You want to setup mysql? Again a single one liner.

The speed to market and deployment is one of the key selling points. You would agree that spending time on plumbing is not worth anybody’s time.

You want to make sure that your application runs on Ubuntu 18 as well as Ubuntu 20. It’s a simple matter of spinning 2 docker images – one with Ubuntu 18 and one with ubuntu 20 and having your application run on both images. Note that this significantly reduces testing time – since containers and dockers are light weight, these can be run in parallel. Again the end result is that your products and projects are more rapidly available to market.

So what do we get beyond ready to market and scalability? Docker today is as secure or insecure as the underlying OS. In the olden days, Docker was considered insecure because of “run as root” – this is no longer true. In next series of articles we will cover the hows and whys of securing and managing a docker infrastructure – briefly covering on topics like Docker Swarm and Kubernetes and then seeing how the whole ecosystem looks like.

If this sounds interesting, sign up for an account at (account creation is free – repositories are not – so be careful). If you do not want to do so, you can head over to – it provides you a cloud based playground to learn more about docker. Have fun Dockering !

Source – The images used in the blog are from a course previously designed and published by the author himself.

Automated Pathogen Detection is set to transform pathology and here is what you should know about it

52-year-old Rakesh Singh walks into a primary health care center in rural Rajasthan with what seems to be a suspected case of Tuberculosis. After hours of waiting in line, his sputum sample is collected and sent to a lab for a sputum smear microscopy. Days go by but Rakesh’s results turn out to be inconclusive, and his diagnosis and line of treatment are subsequently incorrect. Although Rakesh’s case is fictional, the reality of TB in India isn’t. According to the WHO, India recorded about 2.69 million cases of TB in 2018 and the country’s caseload is the highest in the world.

What if there was a way to make TB diagnosis better, faster and error-free? What if there was a way to fine tune the process of sputum smear microscopies? Here is where Artificial Intelligence (AI) and Automated Pathogen Detection hold some answers.

Automated pathogen detection might sound like futuristic words out of a medical lexicon, but thanks to advances in AI, it is fast becoming a reality in laboratories across the world. In essence, Automated Pathogen Detection is a process that combines the power of AI and automation to help test samples of human tissue, sputum etc. in a faster and more accurate manner by eliminating the need for manual human labour. Take for example the process of a Sputum Smear Microscopy (SSM), which is still the primary method for diagnosis of pulmonary tuberculosis in developing countries like India. For an SSM, sputum collected from a patient’s lung is placed on a slide and stained to highlight the bacteria which are then counted by hand. The process of counting thousands of tiny strains is extremely tedious, manual, and time-consuming.

Automated pathogen detection that is aided by advanced artificial intelligence offers a revolutionary solution to this. Vision-enabled AI software can help analyze microscope output that is fed from digital cameras as video. The video is then converted into a series of images and the bacterial load is identified and counted from these images. Aided by AI neural networks and a workflow that is augmented by vision intelligence, the possibility of human error is completely weeded out and samples can be tested 24×7 at a much faster rate. Tuberculosis is just one of the myriad examples; AI-augmented workflow is now also beginning to play a pivotal role in cancer diagnoses wherein tissue biopsy samples can be analyzed more pertinently and effectively, leading to potentially life-saving diagnosis. And in the years to come, AI-aided workflow will find more applications in diagnostic pathology.

One of the main reasons why technologies like Automated Pathogen Detection are finding a stronger foothold in medicine is because they help tackle the decades-long challenges posed by traditional pathology. Medical professionals have been sounding the alarm about scarcity of pathologists and the issues with physical storage of slides in diagnostic pathology for many years now. India, for example, has a load of nearly 40 million sputum samples that are collected annually, and the volume is only set to increase year-on-year. Proliferation of AI-based technology could mean that images of slides don’t need to be physically stored and instead, they can then be digitally archived and even printed in a report. AI-augmented workflow is also largely operator-independent and requires very little human intervention. This could mean that low and middle income countries like India that have a shortage of skilled pathologists need not lose out on high quality and accurate diagnosis. AI-augmented workflow that is empowered by vision intelligence has the potential to address these and a host of other challenges in medicine.

Apart from being a game changer in diagnostic medicine, Automated Pathogen Detection is also the perfect embodiment of the promises that new-age tech, AI, and automation hold. In an article developed by the World Economic Forum, it was envisioned that in the Fifth Industrial Revolution, humans and machines will dance together! This of course is metaphorical, but it perfectly encapsulates the essence of technology such as Automated Pathogen Detection that is not meant to replace pathologists but instead support them and help them make rapid and accurate decisions that can save lives.