Video labeling for machine learning

Video management is a highly topical and demanding task: the endless stream of user-generated video files and surveillance systems recordings has to be categorized nowadays. This necessitates a smoothly running classification solution that facilitates finding the desired videos quickly and efficiently.

The video annotation process is designed to provide assistance in this regard. The method involves labeling or tagging video clips that are utilized to train computer vision models to recognize or identify objects. As opposed to image annotation, video labeling entails frame-by-frame annotation of items to make them recognizable to machine learning models.

What is machine learning?

Machine learning (ML) refers to a subgroup of AI that enables the recognition of patterns and accurate predictions by programs. Due to ML, now we have self-driving cars, e-mail junk filtering, traffic recognition, and more. High-quality ML models can be trained by providing them with accurately annotated training data.

The performance of the ML model is highly sensitive to the quality of the input data that an ML engineer employs to train the model. Hence the importance of accuracy in the data gathering, as well as the labeling and selection of this data. Unfortunately, computer systems have not yet learned to fully process such information on their own, and therefore they can't do without human help, particularly without the annotated data essential for machine learning.

In other words, today's ML models generally operate on the principle of a trainable algorithm that conducts analysis based on a vast array of data. In machine learning, the most frequent type of model training is supervised learning. The supervisor here is either a control dataset or whoever has indicated the appropriate solutions on the given dataset.

The models require labeled data in order to learn. Therefore, a simple unlabeled dataset will not suffice, all the gathered data must be tagged or annotated. ML relies on the learning process to extract different patterns from the annotated data. Human steps in here and utilizes specific software to tag all the relevant content.

What is data labeling?

Data labeling (or annotation) refers to the act of spotting objects in raw data, for instance, in video or images, and adding labels or tags to them as well thus helping machine learning models arrive at accurate predictions and estimates. Annotating data, for instance, can assist self-driving vehicles with stopping at crosswalks, help digital assistants detect voices, as well as aid security cameras with tracking down suspicious behavior.

In most cases, specialists employ special data labeling programs to ease the process for humans. Labels attached to the content reveal to the model what each item represents, allowing the model to train itself based on the example and then, without any hints, identify the desired item on similar types of data.

Suppose a machine model has to identify traffic lights in a video. Thus, the training dataset will be composed of many videos with the traffic lights labels in it. The annotators highlight the signs and characteristics of the traffic lights in the dataset, which in our case are video files and images of separate frames. This will help the model analyze the info and identify patterns in order to perform precise predictions on new, relatively similar input data in the future.

How does data labeling work?

Developing a machine learning model is typically not a speedy process, involving a whole range of activities. The sequence below shows how to annotate a video, image, or any other type of data to get a dataset for machine learning:

Collecting data

The initiation step of a video annotation project (or any ML project) is to gather the appropriate quantity of raw data (images, audio files, videos, texts, etc.). This step involves preliminary processing of the materials, for example, eliminating noise on the video.

To learn, ML models typically require a huge quantity of training data, so that they get as much useful information as possible to ensure efficient and, most importantly, error-free operation. More often than not, such datasets are not readily available, especially for tasks which are rather specific in nature.

It is particularly crucial, for instance, in auto-piloted vehicles, which recognize emergencies on the road, or in computer vision systems used in medicine. Therefore, this stage occupies a considerable amount of time.

Data labeling

That's the primary component of the whole endeavor. Professionals (for instance, data scientists or ML engineers) take into account the task goal and the content of the data, and decide on what kind of labels and data they need to have in the end. For example, they define a set of classes for objects that they need the model to be able to detect, such as people on the road, traffic signs and lights, etc.

Then the labeling begins. Labelers (it can be the data engineers themselves, experts in the particular field, or crown annotators) usually work with a dedicated annotation tool and add meaningful context to the dataset, the context that the model can utilize as reference data. This could be, for instance, video annotations that define the objects depicted there. For example, annotation specialists highlight all the traffic lights in a video shot and attribute the associated label to it. This is how humans help the computer to determine where and in what position the traffic lights are in the video footage.

Quality Control

Continuous quality assurance inspections must be implemented to verify the accuracy of the labels and to allow their optimization. There are various quality control methods, such as including specific tasks to check that the labeling has been done correctly, or aggregation of the results of the same task labeled by several people.

Training and testing of the model

This process usually involves feeding the model with labeled data, and then running tests on an untagged dataset to see if the model provides the expected predictions or estimates.

It is worth noting that the creation of a high-quality ML model involves very large or very specifically selected collections of data. Annotating it manually is a very thorough process that consumes many hours, months, or even years.

However, in addition to manual annotation, automatic annotation is also possible, with special software developed as a result of manual annotation efforts. Labels may be automatically determined and included in the dataset with the help of a technique called active learning. Essentially, experts build an AI model of automated annotation, which labels raw, untagged data. After that, they assess whether the model executed the labeling properly. In the case of errors, ML engineers fix them and re-train the model.

What is video annotation?

Video annotation (also called labeling or tagging) is a form of data annotation where tags and labels are assigned to objects in the video. It provides artificial intelligence a means to learn to distinguish and locate items, patterns, and many other things in videos. Be it training autonomous cars to detect roadblocks, pedestrians, and obstacles, or identifying postures and actions, video annotation helps produce datasets which power the training of many computer vision systems which rely on machine learning.

Methods of video annotation

Unlike image annotation, the task of annotating video footage is trickier, since video annotation involves placing tags not on a single photo or picture, but on every frame of video information. A one-minute-long video at 30 FPS includes 1,800 such images. Nowadays, specialists most commonly implement this method in datasets with a small number of video files showing more static images, since it is easier and faster to label individual objects on the frames in such files.

This single-frame method is, however, deemed extremely time-consuming, therefore, with the development of technology, there is another method for rapid annotation called the Continuous Frame Method, also known as multi-frame or stream annotation. This mode utilizes special annotation tools that enable faster and better labeling of specific objects in the video.

The annotators tag an object not in a single frame, but rather in what is called a stream of frames. Automated tools such as machine learning models, which have been previously trained on a large amount of data labeled by means of the first method described here, help them do this. They automatically track objects frame by frame and do not mistake them for different items, even when they disappear from the frame and then return some time later.

The stream method is only possible due to the advancement of automated annotation tools, which in turn would be impossible without traditional manual annotation. With this approach, however, human involvement is still necessary for quality control, since the accuracy of such annotation may decrease, for example, if the quality of the submitted video is poor, such as night-time surveillance camera footage.

Video annotation process

Video annotation has a primary application in the field of computer vision. Computer Vision (CV) describes the machine learning and computer science discipline that enables processing machines to perceive the world by identifying visual imagery and recognizing things, similar to how humans do it. This technology represents a branch of artificial intelligence.

The power of computers to actually be able to see objects essentially consists of the fact that machines have the ability to extract significant information from visual data like videos, somewhat mimicking the visual perception of a human being. This requires video annotation, the process of tagging video frames in the form of static depictions retrieved from video footage or stream frames, for computers to do this. Video annotation is a crucial process in making computer vision possible.

Rather often modern companies that implement ML have to face computer vision tasks necessary for video and image analysis in order, for instance, to assess the quality of products or security measures implemented at the facilities. Such CV tasks are most commonly solved with convolutional neural networks. This is a form of machine learning, similar to the way the human brain works. Neural networks can only learn and work more or less like a "black box", it is impossible to program them or to know exactly how they work on the inside.

The basic principle of the neural network is to transform the input information into some result. In our case, it could be tagged as video material or a specific conclusion based on the processed video, for instance "this video contains suspicious behavior". Based on the results of the algorithm specialists are able to conclude if the neural network is processing input data correctly.

In case the results of applying neural network algorithms are incorrect, the model will have to be retrained and all errors will have to be corrected. Therefore, the basis for any network is, first of all, properly selected and processed training data.

To train convolutional neural networks, the labeled data is provided in the necessary format with various types of labels, depending on the problem to be solved. Such labels allow the algorithms to remember the shape, color, and outlines of items and subsequently to find them in new images that will be transmitted to the system.

As mentioned earlier, creating a successful machine learning model requires a large and accurate dataset of tagged data. Currently, humans & machines have already created massive datasets that are suitable for training new ML systems, but they are not always sufficient. It may happen that a dataset with the necessary characteristics, for instance, such as a labeled moving fish in an aquarium, simply does not exist or no one has shared it. Then you may need to gather and label such a dataset for your project, using the one-frame or stream method described earlier.

CV would not have existed if it had not been for trained neural networks, which, in turn, would not have existed without the data annotation. Different kinds of video & image annotation may be used by Computer Vision specialists, as appropriate to their task.

Video annotation tools and types

We can say that the types of annotation employed when tagging videos are similar to those used when tagging images. Video annotation software provides specific tools, but the most common types are as follows:

Bounding boxes

Bounding box annotations represent rectangles or squares traced around items to specify their location in the image. This box is tagged, enabling the machine learning model to figure out what's in front of it and to recognize the same object in another video in the future. Bounding boxes are probably the simplest and most common type of annotation. Such a universal labeling technique is suitable for all objects in the video that have no other distinct objects in the background.

Polygon Annotation

Polygon annotation involves polygonal enclosing zones that define the position and shape of items. Given that non-rectangular shapes are more frequently encountered in the physical world, polygons make a more appropriate option for annotating videos than bounding rectangles. They are more adaptive and are more capable of adjusting to the object. Data annotators might employ a greater range of lines and angles when operating with polygons, altering the vertical orientation to more precisely indicate the proper shape of an object.

3D cuboid annotation

3D cuboids allow an annotator to specify not only the width and length of a particular item but also its depth. Therefore, labelers may indicate a volumetric feature in addition to the positional one. Despite the fact that this type of annotation gives a more complete representation of the object, it is not always easy to execute, as part of the object may be hidden from the eye or cropped from the frame.

Key Point Annotation

Key point annotation employs attaching dots to an image and joining them with ribs. This results in X and Y coordinates of the key spots, all indexed in a certain manner. This methodology is employed to distinguish small objects and shape variations that exhibit identical morphology, such as facial expressions and attributes, parts of the body, and people's body postures. This annotation is particularly helpful for applying face filters to a photo or video in an app. In this case, machine learning algorithms track certain points on the face in order to correctly apply a digital mask to the person's head.

Semantic segmentation

Semantic segmentation separates an image into distinct blocks of pixels. Each cluster pertains to a given object and is emphasized by a contour, generating a filter mask of color. Typically, it is employed in situations that not only require the determination of the position of each object in the frame, but also the estimation of the exact pixels of each object. The output is usually a PNG image containing the colors of each class.

This method is regarded as an extra precise annotation since each pixel needs to be given some class. It performs a pixel-based image interpretation, which makes it very distinct from methods that perform object detection. Semantic segmentation requires considerable effort because every pixel is annotated, but with digital tools, luckily it is sufficient to identify the edges of an object, and the program itself will tag the pixels.

Video annotation projects and applications

Annotated videos have a broad spectrum of applications in a multitude of domains. Video data annotation is primarily necessary for the purposes of computer vision, a discipline that makes it possible to detect objects and incorporate so many useful things into our daily lives.

In the medical field, for example, certain neurological and locomotor disorders may be identified via computer vision, sometimes without analysis by a physician. Computer vision algorithms examine a patient's body movements and assist doctors in diagnosing diseases with greater accuracy.

It is possible to monitor animals by employing new systems that utilize object tracking and have been trained to identify the animals' species and their behavior. Remote monitoring of domestic animals may be beneficial in agriculture for detecting diseases as well as behavioral shifts.

The self-driving automobile is a prime example of an artificial intelligence application. It possesses several machine learning objectives, with computer vision being an important part of its solution.

The algorithm steering a self-driving car has to constantly obtain information about its current environment. It has to be aware of the course of the road, the location of other objects nearby and their speed, and the distance from potential obstacles, among other things, in order to continuously adapt to the ever-changing circumstances. The engine handles a continuous stream of video data captured from a multitude of cameras placed throughout the entire vehicle.

Video annotation projects are also beneficial in retail stores to gain insight into customer behavior. Facial features and emotion recognition are implemented in cybersecurity to enable AI software to ID people in surveillance images and CCTV footage.

However, these are just a few examples of the ways video annotation is applied, moreover, there will be even greater numbers of labeling methods over time, as the technology is continually evolving. Technologies such as computer vision, machine learning, and artificial neural networks in general are our future, and they cannot be implemented without properly labeled data.