In recent times, video analytics
is also referred to as intelligent video analytics or video content analysis. It has attracted attention from the industry and the academic world. Thanks to the considerable growth in deep learning, video content analysis has emerged with the automation of tasks.
The latest improvements in video content analytics have been a turning pointer. It does much more like counting people at events, automatic license plate recognition, facial recognition, or smart parking.
Although this technology is wonderful, can you imagine how it works?
This blog will give you a basic understanding of the video analytics concept and its applications in real life to automate processes and get valuable insights.
Define: Smart Video Analytics
Video analytics primarily aims to identify temporal & spatial events in videos automatically. A few examples of video analytics detection include non-compliance with traffic signals, the sudden appearance of smoke and flames, and suspiciously moving persons.
Real-time video analytics & video mining
These systems usually conduct real-time monitoring in which objects, movement patterns, object attributes, or behavior-related to the environment are detected. The video content analytics can even be used to evaluate past data to mine insights. This analysis task can detect patterns and trends that respond to business questions like:
How many times does the red light run, and what are particular license plates of the vehicle doing it?
When is the user presence at its peak in the store and their age distribution?
A task that has been around for approximately 50 years. The idea here is simple- install cameras to facilitate human operators to manage the happenings in a room, area, or public space.
In reality, this task is not so simple. An operator is mostly responsible for more than 1 camera, and more number of cams can affect the operator's performance. This is because humans have limitations, unlike machines.
Video analysis software can help significantly by dealing with volumes of information.
Video Analytics with Deep Learning
Machine learning and deep learning approaches have transformed video analytics. The use of DNNs - Deep Natural Networks has enabled to train video analysis systems that imitate human behavior, resulting in a paradigm shift. It began with computer vision techniques and shifted to systems able to identify particular objects in a picture and tracking their path.
For instance, OCR- Optical Character Recognition has been utilized for years to extract text from pictures. In principle, it was enough to apply OCR computations directly to a license plate's picture to recognize its number.
Real-life use of this would be identifying license plates in the parking areas, where the cams are placed near the gates and could scan the license plate when the car stops. However, it is unreliable to run OCR on images of a traffic camera continually. For instance, if the OCR gives a result, how to make sure it really corresponds to a license plate.
In this new paradigm, models based on deep learning
can recognize the accurate area of a photo in which a license plate emerges. Based on this information, OCR is applied to the specific region in question, leading to reliable outcomes.
In the past, healthcare institutions have invested considerable funds in video surveillance solutions to guarantee their visitors, staff, and patients' safety. Infant abduction, theft, and drug diversion are a few common problems observed by video surveillance.
Apart from that, video analytics enables further exploitation of data to achieve business goals. For instance, a video analytics solution could detect an unchecked patient and alerts the staff.
At home-monitoring also this can be of enormous help. A video analytics solution can process the home camera signals to detect if a person has fallen. The system could also determine if a person took their meds when they were supposed to.
At mental healthcare, video analytics systems can examine facial expressions, gaze, and body posture and detect emotions from body language, offering them objective information to verify their hypothesis or give them hints.
The use of video analytics and machine learning is essential in retail.
State-of-the-art algorithms can identify faces and ascertain people's attributes like age and gender. These computations can also track customer's journeys through stores and examine navigational routes to detect walking patterns. With gaze detection, retailers can know how long a customer stares at a particular product and ultimately answer essential questions.
Video analytics are also remarkable for building anti-theft mechanisms. For example, face recognition computations can be trained to identity shoplifters or spot in real-time if someone is hiding an item in their bag.
Facial & license plate recognition (LPR) techniques can be utilized to recognize people and vehicles in real-time and make the right decisions. For example, it's possible to find a suspect in the real-time and in-stored video footage or identify authorized personnel and permit access to a secured facility.
Crowd management is another critical function of security systems. The advanced video analysis tool can make a huge difference in places like hospitals, shopping malls, airports, and stadiums. These tools can render an estimated crowd count in real-time and activate alerts when a limit is reached. They can also examine the crowd flow to detect movement in prohibited locations.
Moreover, video content analysis is also trained to detect specific events with a high degree of sophistication. For instance, detect fires asap. In the case of airports, when someone enters a prohibited area.
Video analysis solutions have several uses, and the above ones are just to name a few. They can help us in our everyday tasks. Many sectors can benefit from this technology, from smart cities to security controls, at airports, hospitals, tracking people at retail and shopping malls, and more. The processes in video analytics are more effective and less boring for humans and less costly for companies.
Hopefully, you enjoyed this blog, and it helped you understand video analysis and its uses in the real world. You can leverage this tech in your business to automate processes and get valuable insights to make better decisions. HData Listed One of the Trusted Big Data Analytics Companies by Top Mobile App Development Companies.
Harnil Oza is a CEO of HData Systems - Data Science Company & Hyperlink InfoSystem a top mobile app development company in Canada, USA, UK, and India having a team of best app developers who deliver best mobile solutions mainly on Android and iOS platform and also listed as one of the top app development companies by leading research platform.