April 13, 2022

Video Annotation For Computer Vision Training: Why You Should Start Using It Now

Casey Chang

When training AI models, most people focus on annotating JUST images and forget about the option to annotate videos (FYI, CrowdAI supports both media types). However, using properly annotated videos to train computer vision models often accelerates a company’s path to operationalizing models. Below are just a few unique benefits to simmer on to help you define your path from model to value.

1. Faster Annotation

Compared to images, 100 frames of a video can be a lot easier to annotate. Really, how you may ask?

Well, think about a collection of photographs in your 2021 scrapbook. Each picture in the book has a unique background (e.g. a selfie on a hike in the glaring sun and a candlelit dinner in low light) and was likely shot at different times in the day. The same is rarely true of a video. If we have a video of a ball rolling down a hill, a lot of elements in the background stay constant and the ball moves only so far in each frame—there’s lots of redundancy between consecutive frames, and many elements of the background stay the same. This makes annotating the object of interest in each frame relatively simple.

Because consecutive frames are very similar, it is oftentimes MUCH faster to annotate 100 frames of a video than to annotate 100 unique images. With special tools, we can even automate some of the labeling process. In one such example, we only have to annotate a few frames, and our platform automatically annotates subsequent frames for the same object (this is called linear interpolation). This speeds up the annotation process since we only have to make sure AI-generated annotations are accurate.

Comparison infographic of image data and video data with a small stack of squares representing images and a tall stack of photos representing video data.

2. Videos tell a Richer Story

Unlike static images, an object of interest is constantly in motion in videos. This makes videos extremely context-rich. For example, if you want to figure out if you are displaying the proper form in your morning jogs, a video of you running probably is more informative than a snapshot image. In this way, videos allow us to view changes in objects over a period in ways that images cannot.

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3. Higher Consistency and Accuracy

As mentioned above, videos allow us to use a trick called linear interpolation to speed up some of the manual work required for labeling large videos. Not only does interpolation make annotating faster, but it also results in more accurate and consistent annotations. This is because it is easier for a computer to apply consistent logic when tracking an object across multiple frames than for a human to annotate many photos consistently. Thus, video annotation can lead to more consistency and accuracy than images!

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At CrowdAI, we have embedded our years of experience working on diverse AI projects across industries into a single platform experience. Recognizing the advantages of video annotation can help you make the most of our platform and plan a quick path to impact-delivering computer vision. Now, you can start your image or video annotation journey today by taking advantage of our free CrowdAI Explorer account.

May 22, 2023

“Small Devices, Big Impacts: Streaming Computer Vision Models at the Edge”

Running a computer vision model on a cell phone or mobile device is a powerful tool that can enable real-time analysis of images and videos, which can be useful in a variety of applications. While there are challenges to streaming computer vision models on small devices, CrowdAI has developed a roadmap of techniques and tools to overcome these challenges. By leveraging cloud driven API connections for invoking inference from a trained model, CrowdAI sees a pathway to real-time analysis of imagery and video on small devices operating at the edge. Additionally, the geospatial benefits of building models from media captured on cell phones can offer unique advantages for training, monitoring, and analyzing objects of interest.

Zeke Foppa and Taylor Maggos

May 8, 2023

Deploy Anywhere; Use Every Camera: The Power of the CrowdAI Platform

In today's world, where we are surrounded by computers and cameras of all types and sizes, it's essential for machine learning services to be deployment-agnostic and camera-agnostic. Being able to work in any cloud, hardware, or software environment; and to use any camera or sensor is an invaluable advantage that has become increasingly important in recent years as the use of cameras has exploded in various industries. These features allow for greater flexibility and ease of use—exactly what CrowdAI strives to provide—enabling ML to be used in a wider range of applications.

Patrick Collins and Taylor Maggos

May 1, 2023

Exploring how SAM and GroundingDino Increase Opportunities to Accelerate Semi- and Fully Automated Bounding Box Data Labeling

Going from a complex segmentation model to a simpler bounding box object detection model using SAM may seem like a bit of overkill, but there are some instances where an object detection model is favored over a segmentation model. For example, if we have a photo of a street with a bunch of pedestrians, a detection model can provide insight into how many people are there, their location in the frame, and how they interact with each other; segmentation masks wouldn’t give us as useful information since they would just be silhouettes of standing or walking people. Another benefit is that object detection models are designed to be more robust to variations in object size, rotation, and aspect ratio, making them ideal for identifying objects with diverse geometries. Lastly, when computational resources are limited, object detection models tend to be less computationally intensive than segmentation models, which can require more processing power and memory to run efficiently.

Zeke Foopa and Taylor Maggos