Video Analytics was invented with a motive – to help in reviewing the growing hours of surveillance video that a security guard or a system manager (or a human) may never have time to watch. Video Surveillance systems equipped with Video Analytics can help us in finding those minor details that can’t be perceived by naked eyes. Video Analytics or Video Content Analysis is computerized video footage analysis that uses algorithms to differentiate between object types and identify specific behavior or action in real-time, providing alerts and insights to users. Since Video Analytics is based on the technology of Artificial Intelligence, experience plays a significant role. A highly trained model can see through very minute details in video footage.

This technical capability is being used in a wide range of domains, including entertainment, health-care, retail, transport, home automation, flame, and smoke detection, safety, and security.

Video Analytics relies on useful video input. To make the video useful, following techniques are implemented for increasing the quality of the video recorded:
1. Video Denoising
2. Image Stabilisation
3. Unsharp Masking
4. Super-Resolution

What are the Commercial Applications of Video Analytics?

CCTV Systems – This is the most widespread application of Video Analytics. VCA(Video Content Analysis) is distributed on the cameras (at the edge) or centralized on dedicated processing systems. These CCTV’s, for example, can be used to detect and report any suspicious activities of shoppers in a store. Another popular example, is the PIDS(Perimeter Intrusion Detection System). It is deployed in areas whose perimeter can extend to a large radius, like airports, seaports, and the railways. With this technology, we are able to track any intrusions in real-time, giving us sufficient time to react.

  • Traffic Systems – Deployment of Video Analytics on busy squares in crowded cities of the world can be a massive time-saver for the people and the government. At peak times of the day, when the traffic is very high, specialized use of analytics can be used to avoid congestion.


  • Counter-Flow Detection – Walking against the flow in specific locations, such as the airport security checkpoint and gates, can be a sign of some danger. It can potentially result in terminal shutdowns. The wrong entry of vehicles in a one-way can lead to congestion affecting a large number of people. These faults can be quickly responded to by the uses of video Analytics.
  • Suspect Search – The data of facial-recognition can be aggregated along with video Analytics for the detection of criminals at high-security places like the airports’ immigration counter, the baggage collection facility, taxi stands, etc. This can lead to the smooth and swift arrest of such people or elimination of such objects. Time is the essence when looking for something critical.


  • Long Queue Problems at the Shopping Centres – In the densely populated countries like China, India, the crowd increases significantly in stores during the festive season. Trends in data can be used to analyze the crowd and arrange for particular changes for a short duration of time, increasing the store efficiency, and saving the time of the people.
  • Reducing Retail Shrinkage – Retail and logistics companies can use video surveillance analytics to minimize inventory loss significantly. The model is trained to detect unusual activities like unexpected times of presence, unauthorized access, or any suspicious movement of inventory and more.


  • Improving Patient Satisfaction – Video analytics can help hospitals and dispensaries to improve the overall patient experience. Artificially engineered cameras can continuously monitor patients waiting to meet the doctor and ensure they are checked-in within a given time duration. Even an alert can be sent to the staff regarding a patient who has been left unattended for a long time.

Video Analytics is the smart way of engaging customers, reducing wastage of time and improve security. Video data collected is massive and it would be practically impossible for a human to replace a computer. With the fast pace of life and the amount of video content today, using video analytics is a lifesaver for different fields.


What is AutoML?

Automated machine learning (AutoML) is the term used for the technology automating the end-to-end process of applying machine learning to real-world problems. A typical machine learning problem requires a dataset that consists of some input data on which a training model is needed to be built. The input data may not be in such a form that all machine learning algorithms may be applied to it. An ML expert needs to implement the appropriate procedures (including data preprocessing steps, feature scaling, feature extraction), resulting in a dataset suitable for machine learning. Building the model involves the selection of the best algorithm for maximizing performance from the dataset. Many of these steps are often beyond the abilities of non-experts. Considering this in mind, AutoML was proposed as an Artificial Intelligence-based solution to the gruesome challenge of applying machine learning.

What is the Need for AutoML?

The idea of AutoML took off with the development in the field of Artificial Intelligence. It all took shape when Jeff Dean, Google’s Head of AI, suggested that “100x computational power could replace the need for machine learning expertise”. This raised several questions:

Do hundreds of thousands of developers need to “design new neural nets for their particular needs,” or is there an effective way for Neural Networks to generalize similar problems? Or can a large amount of computation power replace machine learning expertise?

Clearly, the answer is NO. Many factors support the idea of AutoML:

  • Shortage of machine learning expertise
  • Machine-Learning expertise is cost-inefficient

For large organizations requiring high efficiency, AutoML cannot replace a machine learning expert, but it can be cost-effective and can be useful for smaller organizations.

Applications of AutoML

AutoML can be used for the following tasks:

  • Automated Data Preparation
    It Involves column type detection, intent detection, and automated task detection within the dataset.
  • Feature Engineering
    It includes Feature Scaling, meta-learning, and feature selection.
  • Automated Model Selection
    AutoML can help in model selection.
  • Automated problem checking
    Problem checking and debugging can be automated.
  • Automated analysis of results obtained
    Applying wonders of AI can save time and capital.

Popular AutoML Libraries like Featuretools, Auto-sklearn, MLBox, TPOT, H2O, Auto-Keras are the ones contributing to enhanced AutoML experience.

Advantages of AutoML

  • The installation of the libraries is effortless.
  • The introduction of Cloud AutoML has speeded up the development of AutoML.
  • Cost-effective, and Labour-efficient.
  • Require a lower level of expertise.

Limitations of AutoML

Although coming with a set of advantages, advanced AutoML introduces the concept of hyperparameters, which are itself needed to be learnt. AutoML can be usefully incorporated for doing a task that can be generalized, but for functions that are unique and require some level of expertise, AutoML turns out to be a disaster.

Future of AutoML

Automated Machine Learning (AutoML) has been gaining traction within the Data Science community. This surge of interest is reflected in the development and release of numerous open-source AutoML libraries, which are mentioned above, and on the emergence of businesses focused on building and commercializing AutoML systems (like DataRobot, DarwinAI,, AutoML is a hot topic for the industry, but it is not all-set for replacing data scientists from existence. Besides the difficulty of automating many of the data science tasks, its sole purpose is to assist data scientists and free them from the burden of repetitive, and less demanding jobs that can be generalized, so they can invest their time on tasks that are more challenging, creative, and harder to automate.

Concluding, we live in an era where the growth of data beats our ability to make sense of it. AutoML is an exciting technological field that has been in the spotlight and which promises to mitigate this problem through the development in the sector of Artificial Intelligence.

We expect significant strides of progress in this field in the near future, and we recognize the help of AutoML systems in solving many of the challenges that we face out there.