Thursday, 8 October 2020

AI for workplace safety and productivity: how does it fit?

- Apoorva Verma

Even with safety regulations and procedures in place, accidents and incidents remain a regular occurrence and continue to be a major challenge for employers, in almost all industries, across the globe. The future outlook of moving towards an AI-enabled future is here. Today, certain domains under AI come into play that can predict accidents and mishaps slightly before they occur.

The advent of COVID pandemic has catalysed the adoption of AI amongst businesses. As most businesses are under the pump to respond effectively to the post-COVID-19 environment, they aim to take all possible positive steps to maintain the safety and well-being of their workforce. However, the challenge is to manage workplace safety and productivity with their existing portfolio of assets, in some cases with even fewer resources and budget. In such a scenario, knowing how workplace safety can be enhanced with AI can be helpful for most employers.


AI provides us with more data and intelligence than ever before, and our ability to utilise this data could enhance business operations manifold. There is a subdomain of artificial intelligence (AI) called computer vision that can be best used for workplace safety and productivity management.

So how does it work and is it easily implemented? 

Most people are familiar with CCTV cameras, they are omnipresent and quite practical for a range of different industries. 

We have developed IRIS, our computer vision technology platform that analyses live video feeds from these CCTV cameras using deep learning and neural networks. It has use cases across various industries and verticals.

IRIS has allowed for greater understanding of work environments and even reducing accidents by a significant margin. It can also be integrated into manufacturing maintenance, quality and production systems to alert workers when they need to check a machine or perform a quality check. 


It raises alarms according to the use case it’s trained for – such as identifying PPE and face mask compliance, social distancing, damage, pilferage, oil spillage, fire, potential fall of a heavy object from height, an unauthorised entry in a restricted area, and so on. IRIS also generates hidden insights on efficiency and productivity in terms of space utilization, MHE utilization, machine utilization, etc. You can get an overview of various other use cases here.


Alarms are raised via WhatsApp, push notifications, or to a control room, along with an image and location making it easier to pinpoint where the incident may have taken place and to enable a faster response. A comprehensive data analytics is available to stakeholders via the IRIS app.


It can bring benefits in terms of cost reduction, improved efficiency and advanced workplace safety. Data insight is key for understanding the risks of workplaces and efficiency of various machines, assets and workforce. It enables better risk management decision-making.


Lastly, the ability to monitor occupational fatigue or hazardous environments more efficiently can be a huge value addition for both the workers and the employer. 


Implementing this technology is easy and scalable as it works with any existing CCTV infrastructure and provides the potential for minimising, or even preventing, workplace incidents and injury. It also measures the efficiency and productivity of man, machine, and space without a bias. 


How can we secure your workforce and premises? Get in touch with Integration Wizards to discuss your requirements.

Friday, 21 August 2020

So I heard you can do Computer Vision at 30FPS; I can do 1000.

- Akash James

And there was a man, in a cave, held captive and hooked up to an electromagnet plunged deep in his chest. Hammering his way through, quite literally, Stark, built his initial Arc Reactor and Mark 1 Iron Man suit, using nothing but a bucket of scrap and modern, tactical, self-guiding, explosive payload-carrying arrows, ergo missiles. Over-did it, didn’t I? Mesmerizing to most, the primitive propulsion system for un-guided flight and rudimentary weapons were not striking to engineers like us.

Stark kept going on, adding new capabilities to his armour, reaching peak performance with the Model Prime and finally calling it a day with the Mark 85. (More like Captain Marvel blasted him in Civil War 2 or the Gauntlet irradiated him, based on the cinematic or comic universe you prefer).

Just like arguably the best science-fiction-based inventor, I never stop with my creations and continue over-hauling for higher performance, ’cause I know that there will always be a higher ascension level to reach.

Computer Vision is a field with rapid progress; new techniques and higher accuracy coming out from various developers across the planet. Machines now have human-like perception capabilities, thanks to Deep Learning; with the ability to not only understand and derive information from digital image media but also create images from scratch with nothing but 0’s and 1's.

How did it begin?

Time and again, the higher tech-deities bring me at a point in this space-time continuum where I am faced with a conundrum. My team and I, back in our final year of college, were building a smart wearable for people with impaired vision, an AI-enabled extension of sorts to help the user with recognizing objects, recognizing people, and performing Optical Character Recognition; we called it Oculus. In all honesty, we did not rip it off from Facebook’s, Oculus Rift VR Headset and it was purely coincidental. The AI Engine was comprised of a multitude of classifiers, object detectors and image captioning neural networks running with TensorFlow and Python. With my simpleton knowledge of writing optimized code, everything was stacked sequentially, not allowing us to derive results in real-time, which was an absolute necessity of our wearable. Merely by running the entire stack on the GPU and using concurrent processes, I was able to achieve 30fps and derive real-time results.

Thus, this began my journey of being fast — real fast.

Ratcheting my way through

Fast forward two years to the present, I currently work as an AI Architect at Integration Wizards. My work predominantly revolves around creating a digital manifestation of the architecture I come up with for our flagship product — IRIS

Wondering what exactly IRIS does? (being Deadpool and breaking the 4th wall) To give you a gist, IRIS is a Computer Vision platform which provides our customers with the ability to quickly deploy solutions that monitor and detect violations. People counting and tracking with demographics, adherence to safety gear usage, person utilization, detection of fire, automatic number plate recognition and document text extraction are some of the features that come out-of-the-box. 

Typically, IRIS plugs into existing CCTV networks, rendering previously non-smart recording networks into real-time analytical entities. IRIS uses Deep Learning for it’s AI Engine but the architecture of the pipeline and the neural networks has seen many changes. My first notable architecture involved web technologies, like Flask and Gunicorn, to create APIs, that my worker threads could utilize. This ensured that the GPU was utilized in a better manner. However, this turned out to be moot when a large number of streams were to be processed.

The two primary hindrances were the API based architecture being a bottleneck under higher loads and the Object detection neural networks being heavy. For this, I needed something better, a better queue and processing architecture along with faster neural nets. Googling and surfing Reddit for a couple of days, I came across Apache Kafka, a publisher-subscriber message queue that is used for high data traffic. We retro-fit the architecture to push several thousand images per second from the CCTVs to the neural networks to achieve our analytical information. We devised another object detection model that was anchor-less and ran faster while retaining performance. Of course, the benchmark was against the infamous COCO dataset.
This increased our processing capability close to 200 fps on a single GPU.

The Turning point

Yes, you guessed it, I didn’t stop there. I knew that there was much more fire-power I could get; accessible but hidden in the trenches of Tensor cores and C++ (such a spoiler). The deities were calling me and my urge to find something better kept me burning the midnight fuel. And then, the pandemic happened.

WHO declared COVID-19 a global emergency — it ravaged through multiple countries and fear was being pushed down people’s throats; most offices transitioned into an indefinite work-from-home status and India imposed the world’s largest lockdown. Wearing masks and Social distancing was the new norm and everybody feared another Spanish flu of the 1900s. 

As an organization, we work with AI to be an extension of man, helping the human race to be better. Usage of face masks and social distancing needed enforcement and what better way to do it than with AI? Our stars aligned, the goals matched and we knew what we needed to build. The solution had to be light-weight and fast enough to run on low-end hardware or run on large HPC machines to analyze hundreds of CCTV cameras at once. For this, we needed an efficient pipeline and highly optimized models.

Hitting 1000 with Mask Detection and Social Distancing Enforcement

By now, I had a few tricks up my sleeve. IRIS’ pipeline now harnesses elements of GStreamer, which is an open-source, highly optimized, image/video media processing tool. TensorRT is something we used to speed up our neural networks on NVIDIA’s GPUs to properly utilize every ounce of performance we could push out. The entire pipeline is written with C++ with CUDA enabled code to parallelize operations. Finally, light-weight models — the person detector uses a smaller ResNet-like backbone and our Face Detector is just 999 kilobytes in size with a 95% result on the WiderFace dataset. Our person detector and Face Detector are INT8 and FP16 quantized making them much faster. With quantization and entire processing pipeline running on the GPU, amalgamating these together, IRIS’ new and shiny COVID-19 Enforcer ran at 1000 fps at peak performance for Social Distancing and 800fps for both Social Distancing and Mask Detection.

This allows us to deploy IRIS on smaller embedded devices to provide a cost-effective solution for retail-chains and stand-alone stores while letting us utilize multi-GPU setups to run on warehouses, shopping malls and city-wide CCTV networks making it easier to comply with and deny the spread of infection.

So what’s next?

I am not done. Achieving one milestone allows me to mark a bigger and better goal. Artificial Intelligence is in its infancy and being at the forefront of making it commercially viable and available in all markets, especially India has been mine and my organization’s vision. The endgame is to have AI for all, where people, be it developers or business-owners, have the ability to quickly design and deploy their own pipelines. 

IRIS aims at being a platform to precisely empower individuals with that, with the intention to democratize Artificial Intelligence, making it not a luxury for the few, rather a commodity for all. 

Chiselling AI agents to be the best tool that man has ever known will be our goal, paving the future with a legion of Intelligent agents, not making the world cold, but making us a smarter race. Ain’t nobody creating Ultron!

Thursday, 6 August 2020

What Enterprises can do to adapt to the new normal?

- Apoorva Verma

March 2020 drastically changed businesses, global economy, and every aspect of our daily lives. As business pivoted to home offices a lot of things changed. Now, as economies begin to reopen, “new normal” has become the buzzword that everyone is talking about. Yet, industries such as warehousing, manufacturing, construction, et al have to work with a large number of workers, on a daily basis at a particular premise, as they cannot all be working from home, given the manual nature of their jobs.

The need for risk assessment

This introduces new risks and risk assessment needs to be carefully re-evaluated. Thus, as most such companies try to identify and manage risks within their premises, the advent of COVID-19 has put the health and safety of the workers under even more scrutiny.

Risk assessment can help enterprises put controls in place that can prevent the spread of contagious diseases such as COVID-19 as well as other accidents and injuries. Since all organisations and industries are different, they take different approaches to carry out a risk assessment.

However, you could carry out the process in these five broad steps:

  1. Identify potential hazards in the premises
  2. Identify who could be at risk from those hazards
  3. Implement control measures by managing the risks
  4. Record the findings of your assessment
  5. Review the risk assessment on a regular basis

Also, it is better to involve ground-level workers in this process to ensure that you implement controls that are effective and safe.

Can technology help?

As most premises are fitted with CCTV cameras, it is best to turn these passive tools into active analytical tools. With a surge in the adoption of AI, technology such as computer vision can be deployed to implement and ensure workplace safety.

Computer vision is not only a contactless solution, but it is also free from human errors and prejudices. In addition to marking contactless attendance, eliminating biometric, it can detect face masks compliance, social distancing index, PPE compliance, and more. In case of a breach, real-time alerts can be sent to the right authorities to take immediate action. Such a system can improve operations in warehouses as much as it can ensure compliance in a manufacturing setting.

Since the cameras have constant access to data from live feeds, it can also generate hidden insights on machine utilization, efficiency and productivity of the staff and machines. Win-Win, isn’t it?

Wednesday, 5 August 2020

Biometric is passé, Go Contactless

- Apoorva Verma

Consider a scenario where a large steel plant with thousands of workers has restarted after the pandemic. The bio-metric machine for marking their attendance now becomes a high risk. Thus, given the highly contagious nature of the novel coronavirus, the use of biometric machines has become, more or less, a threat to safety.  

In addition to workplaces, the Indian government has encouraged the use of face recognition technology and it is being explored in airports, railway stations, schools, universities, and other places frequented in groups. For example, Rajiv Gandhi International Airport at Hyderabad recently became the first airport in India to initiate facial recognition while Bengaluru, Manmad and Bhusawal railway stations are in testing stages for implementing the face recognition technology.

In a scenario where touch is avoided at the most, contactless has become the need of the hour for most enterprises. In fact, a spike is predicted in facial recognition technology. 

With the pandemic seeing ubiquitous adoption of masks and other protective gear that may partially cover faces, a solution that can recognise faces even with masks will meet the current demands of the world.

LogMyFace is an app owned by Integration Wizards Solutions, developed to help enterprises manage employee attendance while proving safe and convenient for employees. Unlike a biometric which requires touch, this app marks attendance from phones and tablets.

Using a phone for facial recognition, the employees can log their attendance from any of the predefined locations - be it client location, office, or home.

Also, if synced with the companies’ CCTV cameras at entry/exit, it can recognize faces from a distance of two metres or less. It will track, log, and recognise the face liveness, gender, age and emotions. It offers 98% accuracy and provides reports and dashboards for detailed audits.

Connect with our team today and unlock the possibilities to explore 'contactless attendance' in your organisation.

Thursday, 2 July 2020

Enhancing Workplace Health & Safety Using Computer Vision

Subhash Sharma 

Although health and safety at workplaces have improved over the years, yet the UK continues to have a large number of workplace accidents. The number of accidents resulting in injury, or in some cases, even death is quite high. Many of these accidents can be avoided, and AI-based computer vision can play a significant role in cutting down these accidents. 
Health and Safety Statistics. Key figures for Great Britain (2018/19)
  • 1.4 million working people suffering from a work-related illness
  • 2,526 mesothelioma deaths due to past asbestos exposures (2017)
  • 147 workers killed at work
  • 581,000 working people sustaining an injury at work according to the Labour Force Survey
  • 69,208 injuries to employees reported under RIDDOR
  • 28.2 million working days lost due to work-related illness and workplace injury
  • £15 billion estimated costs of injuries and ill health from current working conditions (2017/18)

( Source: )
Forklifts alone account for 1,300 UK employees being hospitalised each year (That’s 5 UK workers each workday!) with serious injuries due to these accidents. Unfortunately, that number is rising as there is a significant growth in e-commerce and warehouses across the UK. 

Use of AI-based computer vision for optimising workplace health and safety in the UK.
Our Computer Vision product, IRIS, is an AI-based computer vision solution to track and predict workplace accidents and then prevent them from happening. The existing use cases include:
  • Fork Lift Safety, Predicting and Prevention of Fork Lift accidents.
  • Use of Lifting Equipment
  • Work at Height
  • Fire & Thermal injuries and accidents
  • Machine Guarding
  • Manual Handling
  • Monitoring near misses and reporting near misses & accidents in real-time
  • Monitoring Use of PPE

IRIS is an enterprise computer vision solution. The product is currently deployed at many customer sites including Fortune 500 companies. The AI solution sits on the top of existing CCTV infrastructure. It is very cost-effective and can be deployed quickly either at a customer site or through the cloud. 

Reshaping The Retail Industry Paradigm Through AI

- Apoorva Verma

Data, artificial intelligence, automation, IoT, bots, machines, and digital transformation. The buzz words. Except, these are not just buzz words, these are a reflection of a change that’s happening around us and the growing opportunity for innovation. 


Retailers across the globe are facing unprecedented changes in their fundamental business model and operations. Factors such as high competition, changing consumer behaviour, government regulations, tenacious technological development and most recently, the COVID-19 pandemic, are affecting almost all facets of the retail business. 


It has become critical to respond to these changes to sustain and remain relevant. With such diverse factors affecting the retail industry, conventional methods are not going to help differentiate. This perpetually changing landscape can be navigated through data and artificial intelligence (AI) could be the road map for this.


According to research by Accenture, 86% of retailers are experimenting with AI to forge new growth as they agree that it is transforming the industry. There is, therefore, a surge in the adoption of AI.


However, AI is uncharted territory for most businesses. In this scenario, partnering with AI players can provide significant momentum to enterprises - whether they are in the exploratory or advanced stages of their AI adoption journey. We can do this together, without having to build from ground zero.


Transformation with IRIS AI


According to a study by NASSCOM, the retail industry in India is one of the top-five retail markets in the world by economic value and is poised to double by 2024. 


Most retail outlets are equipped with CCTV Cameras. Our AI-powered computer vision product – IRIS syncs with this existing infrastructure to provide actionable insights about shopper, employee, assets, and activity within the store premises. 


It generates detailed insights and reports that cannot easily be generated manually. Analysing live feeds from multiple cameras, IRIS gives insights on various factors such as the overall footfall in the store, demographic details of customers such as male vs female or their age range, the percentage of customers walking out without shopping, heat maps on areas most visited by customers, peak time walk-ins, the average time spent by shoppers, as well as other insights such as identifying stock-outs in real-time and alerting the manager, observing staff behaviour, sentiment analysis and more. 

The analytical information lets retailers optimize their operations to ensure customer satisfaction, manage inventories, store layouts, and much more.


In the light of COVID-19, we have trained our model to identify face mask and social distancing compliance too. 

It sends out real-time alerts to the relevant person in case of non-compliance via WhatsApp, SMS, Push notifications, etc. along with the details and image. Organisations can look at overall analytics for these violations and take precautions.


Incorporating artificial intelligence into your business may seem daunting, but a focused, incremental approach will help deliver value and mobilize your company in a new direction. 


How do we make this easy for you?


The first step on this journey is to define your expectations from the technology. Most of our clients started small and scaled fast. This helped us figure out the pain points and deliver clear and immediate value. 


We train our AI platform IRIS using deep neural networks and machine learning models to generate insights across the big data sets, using live feeds from multiple cameras. As this type of analysis is almost impossible manually, it amplifies key performance indicators for the business. 


Once the use case has been optimized according to your requirements, it can be swiftly rolled out across the organization to maximize the value. 


Start your AI journey with us now to build a long-term advantage over the competition.

Monday, 22 June 2020

Covid-19 isn’t going away soon…… What’s your plan to make YOUR team safe in the workplace?

Thankfully Covid-19 seems to be on the wane…. But it will take a long time to disappear fully, and it is unlikely to be the last pandemic. This means that YOU may need to make the workplace safe for your team and perhaps your customers.

The good news is that many organisations have successfully implemented a Work-From-Home strategy….. And perhaps it has been much easier than we had dreamed it could be – certainly full or part-time WFH will now be a real option for many office workers.

The bad news is that solving the problem for workers who MUST be in their workplace is much, much harder.

Thankfully there ARE tools and solutions (automated Artificial Intelligence) that can help.

Solving the problem falls into several areas

  • Supplying / enforcing use of PPE
  • Providing hygiene solutions – hand sanitiser and washing facilities
  • Making social distancing easy & ensuring that it happens

The last of the above – social distancing, is easy to request, but much harder to implement & has a whole set of sub-problems

  • Filtering out people who have symptoms e.g. high temperature, that they themselves may be unaware of
  • Re-arranging the environment to provide enough isolation where individuals work
  • Reducing hotspots where people might struggle to keep a suitable distance
  • Changing processes to reduce face-face interactions where possible
  • Encouraging a culture where people choose to do the right thing

For these problems we have a technology solution: IRIS, an Artificial Intelligence tool that automatically and constantly analyses what is going on in your workplace AND gives you the data to work out what is going wrong, when it is going wrong and gives you the opportunity to fix it.

  • It can check who is & is not using PPE
  • It can check temperature of people entering a building
  • It can measure how far apart people are 24/7
  • It can find hotspots where social distancing guidelines being broken
  • Feedback is immediate and specific

Crucially this allows YOU to take control

  • Change processes exactly where it creates problems
  • Rearrange workspace to minimise squeeze points & hotspots
  • Make changes AND then check if these changes were effective
  • As with many processes you often get what you measure ….especially when the feedback loop is effective

Perhaps as importantly it allows you to SHOW that you are taking control, hopefully giving confidence to people, demonstrates your organisation’s desire for change and ideally creating a model for the right culture. 

Here is how you can find out more


Ask us at