Why video annotation is crucial for computer vision and deep learning?
A picture is worth a thousand words, but what can you say about a video? Compared with an image, a video is much richer in terms of information and context. It can offer useful data for transportation, manufacturing, and security applications. Now, deep learning and computer vision applications are capable of automating the tasks of reading video data with the help of video recognition and video annotation.
In simple terms, video annotation means labelling every object of interest in a video clip for building a training dataset for a deep learning model or a computer vision model. You can either go frame by frame and annotate the objects, treating each frame as a separate image, or you can proceed by tracking pixels in the video with continuous frame methods like optical flow for better continuity and context.
The benefits of video annotation
Why is it crucial to annotate a video for computer vision and deep learning instead of simply focusing on a more straightforward image annotation? Video annotation offers efficiency and accuracy required for these models, which image annotation does not provide. In addition, video annotation delivers the following benefits:
Ease of annotation and data collection:Videos are nothing but a collection of images and can offer more data for a training dataset through individually annotated images. In contrast to an image, a video also contains different variations of the object of interest with a consistent background. This makes annotation easier and helps you train a robust computer vision model.
Greater context for annotation:A video narrates a vivid story and offers a superior context for annotation. This can include information about the direction and speed of motion, partially occluded objects, and continuity (whether the object was visible in previous frames). Such context contributes to a faster and more efficient annotation process.
More consistency and accuracy:With rich context available for annotation, the resulting dataset offers better training for your deep learning and computer vision model. The linear interpolation in videos can train the models to track the object of interest across multiple frames, offering different variations and orientations of the object for highly consistent and accurate training.
The need for video annotation in computer vision
While the AI wave sweeps the tech world, other complementary technologies must keep up. This rapid development is directly related to the evolution of annotation. Video annotation is bound to many real-world applications across the transportation, manufacturing, security, and sports industries.
Object detection:The primary purpose of video annotation in computer vision is to capture and identify objects. This feature is helpful in computer vision mimicking the human eye and its applications, such as identifying objects on the road for autonomous and smart transportation or identifying defective objectives on a manufacturing line.
Object localisation:Once an object is detected, it is imperative to locate it for any application. Localisation can help computer vision applications identify the boundaries of objects and predict any potential hazards in autonomous and smart transportation applications.
Object tracking:With the help of video annotation, deep learning models can track the movement of the object of interest from one frame to another. In smart transportation applications, this is useful for tracking traffic flows, differences in the surrounding landscape, and changing road signs to react to road dynamics and ensure passenger safety.
Activity tracking:Like object tracking, activity tracking focuses on the human aspect and navigates through human movement in the video. In autonomous driving applications, this can help prevent accidents with the help of a better perception of the surrounding environment. Activity tracking applications can also review safety measures in manufacturing plants and track the actions of athletes to effectively understand, analyse, and improve their performance in the sports industry.
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How can Infosys BPM help?
You can get high-quality datasets for your computer vision and deep learning models with Infosys BPM Annotation Services. With the platform plus human-in-loop service model, leverage the combined capabilities of human intelligence and automation solutions from an expert and dedicated annotations and data science team. Supporting clients for autonomous driving and mining applications, Infosys BPM Annotation Services can help you annotate keyframes and moving objects in videos.