Advances in computer graphics technology has the potential to become a game changing solution to one of the greatest challenges of Video Content Analytics: reliance on large quantities of annotated data. Synthetic data generation may be the answer to accelerating and simplifying Deep Neural Network (DNN) training, making data-driven video analytics an even more efficient way of transforming video data into actionable intelligence.
To effectively power Machine Learning, the Deep Neural Networks that enable Artificial Intelligence-backed technologies need to undergo training. Through the teaching process, the DNNs learn to detect, identify and classify data through exposure to large quantities of information. For instance, to be able to extract all women in a video scene, a Video Content Analytics engine must first be exposed to large quantities of annotated images of women (and other objects that are not women), so that the technology can effectively detect and classify women in future video footage analysis.
Access to training data is a challenge in and of itself, but tagging the different objects in imagery or video footage is an ongoing struggle – especially because Machine Learning’s accuracy is relative to the amount of data provided to train it.
To optimize DNN training, then, two limitations must be overcome:
3D rendering technologies can be leveraged to overcome these formidable challenges, addressing both Deep Learning inhibitors. 3D rendering software enables the manufacture of realistic data based on objects in actual images and video frames. The algorithmically created synthetic data can then be used to train Machine Learning models.
Fabricated data is much easier (and cheaper) to acquire than real data, and infinite amounts of data can be generated to enable Deep Learning and to drive AI-backed technologies. And, because the data is generated based on a specific data point that is already identified and classified, the tedious manual tagging process becomes completely irrelevant: The artificial data is already labeled when it comes into existence.
The benefits of synthetic data generation for accelerating and advancing Deep Learning capabilities are obvious, but this tactic for DNN training is not without its disadvantages. The artificial data can become skewed with a lack of data variability leading to misleading results. If the data created isn’t diverse enough, the Machine Learning technology might not learn to identify an object correctly, having learned only a narrow subset of a particular designation.
Furthermore, if the rendered data isn’t similar to the appearance of the real data, DNNs could be trained based on false information, leading to inaccurate identification and classification.
These are serious challenges, but they too could be overcome using Deep Learning technologies. The 3D rendered data can be reviewed using Machine Learning to identify unreliable data. Based on its assessment, the technology could then be used to apply correct data and make the unreliable data look more realistic, so it can be used to overcome data quantity and tagging issues.
Thus, Deep Learning is a self-perpetuating technology that could be used to better itself and drive additional Artificial Intelligence applications for transforming raw data into actionable intelligence.