What Is VIDEO DATASET And Annotation?

Introduction to VIDEO DATASET

Video dataset is now an increasingly common feature in buses and other public vehicles. A survey carried out by Axis Communications, it was found that around 95 percent of respondents, located in 30 countries, have installed security cameras into their vehicles. But even using video surveillance, it doesn't solve the previously problems confronted by public transportation.

Public transport is an industry that is susceptible to crimes and accidents. Although use of Video Dataset provides insight into the security of vehicles, it isn't able to solve many other issues that arise when traveling. It is also crucial to learn from the experience and apply it to continuously improve the procedures.

What is Video annotation?

Video data annotation is a process by which the annotators label or annotate objects frame by frame to teach the AI how to recognize those objects in the new dataset. The data is annotated by making use of different shapes, drawings, or comments.  This is very similar to image annotation but at the same time, far more complex than image annotation because, in an image dataset, there is less complexity, as there are fewer objects to annotate.  But video is more complex because of the presence of multiple objects and subjects like humans, vehicles, objects, and more.

Also, in the video, the amount of data that has to be annotated is less. Whereas, in the video, the amount of data is large, as the annotator needs to label the video frame by frame.

Here are some industries that make use of VIDEO DATASET:

1. Automotive: The biggest use case in the automotive industry is self-driving cars. The self-driving cars need a huge amount of data in the form of video that can help the car detect people, signs, objects, zebra crossing, other cars, brakes, and more. Another use case in the automotive industry can be making AI find parking spots in the parking lot. The car will analyze the parking lot to find the best place to park the car.  Another use case can be AI detecting potholes and bad road conditions.

2. Gaming: Video game companies use human activity tracking and pose estimation to create games that are highly realistic. This includes accurately annotating things like people's facial expressions and how they pose while performing different actions in games.

3. Medical: The major benefit of AI in the medical industry is to help doctors with the diagnosis and imaging of patients. Video Annotation helps in analyzing mammograms, X-rays Dataset, CT scans, and more to monitor the patient’s progress.

4. Retail: A great example of video annotation in retail is Amazon Go. Customer enters the store and adds things to their cart. With the help of sensors and cameras, the cart will calculate the total price and customers can go without checkout, as the money will be deducted from their Amazon account. How cool is that?

5. Surveillance: Government agencies can use CCTV footage to detect traffic in a particular area, detect potential crimes through video annotation, and check the speed of cars.

6. Agriculture: Through video from drones, the AI can detect different types of crops, and analyze the grain quality, weed growth, herbicide usage, and more. Video dataset can also help the farmer in livestock management.

7. Industrial: Video annotation is increasingly being used in the manufacturing industry to improve productivity and efficiency.

How GTS.AI can help you in VIDEO DATASET and ANNOTATON?

When it comes to video datasets, video and Image Annotation, we at Global Technical Solutions (GTS.AI) have the expertise, knowledge, resources, and capacity to provide you with everything you need. Our team Provides the highest quality datasets and are tailored to your specific needs and problems. We have members in our team who have the right knowledge, skills, expertise and qualifications to collect and deliver video data for any situation, technology, or application. Our multiple verification systems consistently ensure that we deliver the highest quality dataset.

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