Feeling inspired to write your first TDS post? We’re always open to contributions from new authors.
Before we get into this week’s selection of stellar articles, we’d like to take a moment to thank all our readers, authors, and members of our broader community for helping us reach a major milestone, as our followers count on Medium just reached…
We couldn’t be more thrilled — and grateful for everyone that has supported us in making TDS the thriving, learning-focused publication it is. Here’s to more growth and exploration in the future!
Back to our regular business, we’ve chosen three recent articles as our highlights this week, focused on cutting-edge tools and approaches from the ever-exciting fields of computer vision and object detection. As multimodal models grow their footprint and use cases like autonomous driving, healthcare, and agriculture go mainstream, it’s never been more crucial for data and ML practitioners to stay up-to-speed with the latest developments. (If you’re more interested in other topics at the moment, we’ve got you covered! Scroll down for a handful of carefully picked recommendations on neuroscience, music and AI, environmentally conscious ML workflows, and more.)
- Mastering Object Counting in Videos
Accurate object detection in videos comes with a host of new challenges when compared to the same process in static images. Lihi Gur Arie, PhD presents a clear and concise tutorial that shows how you can still accomplish it, and uses the fun example of counting moving ants on a tree to make her case. - Spicing Up Ice Hockey with AI: Player Tracking with Computer Vision
For anyone looking for a thorough and engaging project walkthrough, we strongly recommend Raul Vizcarra Chirinos’ writeup of his recent attempt to build a hockey-player tracker from (more or less) scratch. Using PyTorch, computer vision techniques, and a convolutional neural network (CNN), Raul developed a prototype that can follow players and collect basic performance statistics. - A Crash Course of Planning for Perception Engineers in Autonomous Driving
While we might still be years away from self-driving cars dominating our roads, researchers and industry players have made significant progress in recent years. Practitioners who’d like to expand their knowledge of planning and decision-making in the context of autonomous driving shouldn’t miss Patrick Langechuan Liu’s comprehensive “crash course” on the topic.
Be the first to comment