Video Analytics and Its Scope in Solving Real World Problems

It is interesting to see that innovation never rests, despite the unprecedented situations. Technology evolves and brings better and handy solutions that can simply change human life. Video analytics is among one of the trending and reliable technology-driven panacea to understand and thwart unwanted events.

How Does Video Analytics Work?

The architecture of any use case may vary, but the scheme remains the same. Video content analysis is done in two ways -

• Through configuring systems to trigger alerts for specific events.
• By performing advanced searches to facilitate forensic analysis tasks.

Feeding the System

To have a clear picture of the entire area from all angles where the events being monitored must be apprehended. The more data, the better.

Central Processing vs Edge Processing

Video analysis software runs on servers located in the monitoring station, stated as central processing. Or can either be embedded in cameras through a strategy known as edge processing.

Defining Scenarios and Training Models

After the physical architecture, it is essential to define scenarios that need to focus & train the models that will detect target events. The basic task in video analytics defines -

Image Classification - Selecting the category of image.

Localization - Locating an object in an image.

Object Detection - Locating and categorizing an object in an image.

Object Identification - Given a target object identifying instances in an image.

Object Tracking - Tracking object that moves over time in the video.

Recent Improvements In Video Analytics Solutions

The incorporation of machine learning was long back done in VA. A component of Artificial Intelligence - deep learning in the analytics solution has been the real game-changer. Today's Video Analytics Software not just automates the tasks but counts people at the event, automates license plate recognition, facial recognition, or smart parking.

Since the elementary job of video analytics is to recognize spatial and temporal events in the video, it can be more of assistance.

Real-Time Video Mining

The systems are designed to perform real-time monitoring of the objects, movement patterns, and behavior of people or objects. Howbeit, video analytics is greatly improved to analyze historic data to mine insights and then detect trends & patterns that can easily provide answers to the questions such as:

• Maximum customer presence in the store.
• Age distribution of the customer when there is a crowd within the store.
• The number of times the red light runs.
• Specific license plates of the vehicle that run the red light.

One of the biggest use and exemplary applications of video analytics is video surveillance. The idea is the installation of physical cameras that are operated by humans to control events and incidents occurring in the room, area, or public square.

An operator is essential to monitor cameras, but as the number of hardware increases and generates signals, the challenge increases when the process is handled with human limitations.

Challenges in Building Solutions

Identification of Problem

The issue begins when you are unable to identify areas where solutions accurately work. The real-life scenario areas are non-linear and unpredictable, which creates a sense of technology handicap in developing solutions. The conventional way of solving the processing of digital pictures requires dependency on data sets. You can also aware about How Intelligent Video Analytics Addresses Video Management Challenges.

Implementation of Irregular Data

Another major concern for developing a Video Analytics Solutions follows after the absence of data sets. A solution like Automatic Number Plate Recognition(ANPR) runs effectively only after comparison between data points.

Conventional Methods

No wonder improvements have been made and are visible for image processing algorithms, conventional strategies of SIFT(scale-invariant feature transform) and SURF(speed up robust feature) have greater dependencies for creating descriptor vectors. But, the issue arises when algorithms have limitations while working on non-standards data sets.

How Evolving Video Analytics Software Solves Real-World Problems?

The first concern is non-standardized data sets, which would come up with predefined data points. A way to treat this concern is by inculcating artificial intelligence in the system in order to bundle up all sorts of irregular data points to conclude in a logical sense.

Additionally, a multidisciplinary approach can be integrated within the software and hardware capabilities in developing inevitable security-critical systems.

Video Analytics with Deep Learning

As aforementioned, deep learning capabilities have revolutionized video analytics capabilities.

The use of Deep Neural Network (DNNs) has trained video analytics carriers to mimic human behavior that has displayed a significant paradigm shift. For instance, optical character recognition (OCR) has been used for extracting texts from images. OCR can further be used to capture license plate number by placing the camera in a position to film the license plate.

Here, a real-world application can be recognized by identifying license plates at parking facilities through a camera when a car stops.

New models based on deep learning will identify the exact area of an image in which license plates appear. With such information, OCR is applied to the exact region giving reliable results.

In A Nutshell -

With a plethora of Video Analytics more complex scenarios are visible such as smart homes. In the future, some kind of adaptation or parameterization has to be done in video analytics software allowing full customization of the solutions. You can always turn to video analytics companies in USA for the ideal solution.