Video Annotation for ML Model: Top 5 Challenges and Opportunities

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Video Annotation for ML Model: Top 5 Challenges and Opportunities

Enterprises are poised to leverage videos to capitalize on data-driven opportunities, improve processes, frame strategies, and drive perfect predictions.

Machine learning models and artificial intelligence are dominating each industry landscape. Right from identifying high-value customers in retail stores to crafting road safety policies; organizations have found hidden advantages in the most unstructured data type like videos.

The penetration of deep learning and machine learning technologies like computer vision and facial recognition is thus on the rise. At least the increasing market size of the machine learning market, which is expected to hit $8.81 in 2022 from a mere $1 in 2016, foretells the enormous adoption of video annotation.

Video annotation as a process has its own share of complexities. Here, the success of the ML model hinges on the video tagging and labeling efficiencies. Video annotators have to operate against unforeseen complexities – making video annotation a hard nut to crack.

In this blog, we try to highlight some common video annotation challenges and also have a look at the opportunities that exist in this arena. But before that let’s understand video annotation and the way it works.

What is video annotation?

Video annotation works as a class of techniques that tag and track interest zones in a video. This tagging is enabled by labeling and commenting to locate the target areas for detection of the objects in movement. The annotation techniques encompass this entire process to form efficient datasets for AI algorithms.

How video annotation works

The way video annotation operates differs from the selected technique. In the single-frame approach, the video is decomposed into several images. The obtained images are step-by-step annotated individually. So essentially you are converting the video annotation process into image annotation. This method stands feasible when there is less movement, and you are sure of accurately capturing each target area with a simplified process.

The continuous frame method is another approach. This method involves analyzing streams of videos and annotating target objects. Annotation software applications are leverages to accurately capture video frames from the streams. This approach comes in handy when you need to catch up with the speed of the movements in the video.

Understanding the top 5 challenges in video annotation

Video annotation, by any means, is a tough process to drive. You might have encountered multiple challenges while annotating videos for your AI models. Summarized here are the top 5 challenges in video annotation that AI adopters commonly encounter.

1. Automation Implementation

- Automating video annotation poses manifold challenges. While investing may not be a big deal for you, you need to spend quality time to determine the best tool. A haphazard approach to choosing a tool not only wastes your efforts and resources. Video annotation involves various stakeholders and with automation, you always have to take extra caution that it is not leading to the creation of silos.

2. Managing high annotation budgets

Unlike any other data format, video annotation is highly unstructured. You need a dedicated workforce, specialized treatments, and applications to glean useful insights from videos. All these elements combined costs significantly. Since annotation is never a one-time activity, if you fail to sustain this expenditure on a long-term basis, then the AI modeling process collapses.

3. Managing high video data volumes

Not just the size of data but the exponentially rising data volume is the real cause of making the video annotation process challenging. Artificial intelligence models are dynamic, meaning that they do not demand one-time dataset building, rather they operate on a loop that continuously feeds them data. With time, you need to evolve your database handling to smoothly accommodate data processing considerations.

4. Ensuring annotation accuracy

The character of video data creates new operational challenges for tagging the interest zones. Accuracy is thus vulnerable to even the slightest mistake. The continuous locomotion of objects makes your human annotators continuously move their eyes to not miss the target areas. When you fail to monitor the objects, you create scope for imbalanced classes, adversely impacting the accuracy and precision of the AI model.

5. Identifying the right video annotation provider

Now, what if you decide to outsource video annotation, you do not directly streamline your AI implementation lifecycle. Rather, you make yourself ready to wade through the complexities of selecting the best vendor. There is no dearth of companies that claim to provide quality video annotation services. Each vendor boastfully markets, but never offers concrete proof of the depth and width of their services and quality.

Opportunities to streamline video annotation

However maybe the challenges, opportunities to succeed are always there, only that you need a strong will to discover them. A few of such opportunities in the video annotation space are here.

Have the right workforce

The abilities of your workforce matter and make a difference. But what defines these abilities? First and foremost, the video annotators must provide both qualities as well as quantity, leaving no scope for a trade-off.

Second, they must be mentally able to endure the long-term monotony, highly possible in annotation tasks. As the video annotation project progresses, you can never rule the chance of the workforce making mistakes. Consistency is thus largely governed by the human element.

Scientifically choose a video annotation provider

While we discussed identifying a video annotation provider, following a methodical approach can surely lead to forging a deal with the best video annotators. This approach significantly optimizes the vendor identification and selection process.

Video annotation companies support an inclusive model which lets prospects thoroughly evaluate their strengths. These companies while giving a glimpse of their existing video annotation projects, also execute POCs on real-life datasets from the concerned domain.

Combine automation capabilities with human skills

Offering tremendous promises, automation is making its way. But how far, in reality, has automation progressed? Despite automation talks dominating most areas, the very domain of AI still relies on human interventions for accuracy and precision.

Automation in video annotation is still far from reaching maturity and a strong human-in-the-loop approach can only make you succeed in your annotation goals. Automation augmented by human intelligence is the viable approach and holds relevant for sustainable annotation and algorithmic performance.

Establish precision benchmarks

Video annotation cannot operate in silos. In worst case scenarios if that happens then the AI framework collapses. You must link video annotation accuracy to the accuracy of AI model’s end output. Only by setting precision benchmarks, do you rightly interlink the annotation accuracy to ground truth accuracy, and all other parameters.

The way AI evolves, you have to evolve your benchmarks too. For instance, you first aim to achieve 98% accuracy, you cannot sustain on it, and soon you will have to target 99% and then 100%.

Track macro and micro activities

Video annotation is not just about tracking and labeling target areas, but there are a series of events that happen in the process. You must track and trace the efforts you are investing for preventing misuse and wastage of resources.

Understand how you are acting on technique identification, implementation, tool selection and resource allocation. Check what is your time to label each zone, per hundred, per thousand label time. Use this intelligence to excel operationally.

Conclusion

To scale a video annotation pipeline is to effectively deal with the challenges that cross your path. You need to have a video annotation ecosystem that helps you create, maintain and manage a consistent flow of quality data for generating quality training datasets.

When challenges exist, they give birth to opportunities, which you cannot uncover without making an effort. Don’t link your video annotation success to short-term goals, rather aim to build a pipeline that helps you to secure a long-term advantage. Always be open to joining hands with an expert to streamline the process so as to easily succeed in the competition.