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Before we get into this week’s number of stellar articles, we’d wish to take a moment to thank all our readers, authors, and members of our broader community for helping us reach a serious milestone, as our followers count on Medium just reached…
We couldn’t be more thrilled — and grateful for everybody that has supported us in making TDS the thriving, learning-focused publication it’s. Here’s to more growth and exploration in the longer term!
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 remain up-to-speed with the most recent developments. (In the event you’re more concerned with other topics in the intervening time, we’ve got you covered! Scroll down for a handful of rigorously 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 bunch of recent challenges compared to the identical process in static images. Lihi Gur Arie, PhD presents a transparent and concise tutorial that shows how you’ll be able to 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 searching for a radical and interesting project walkthrough, we strongly recommend Raul Vizcarra Chirinos’ writeup of his recent try and construct a hockey-player tracker from (roughly) scratch. Using PyTorch, computer vision techniques, and a convolutional neural network (CNN), Raul developed a prototype that may follow players and collect basic performance statistics. - A Crash Course of Planning for Perception Engineers in Autonomous Driving
While we’d still be years away from self-driving cars dominating our roads, researchers and industry players have made significant progress lately. Practitioners who’d wish to expand their knowledge of planning and decision-making within the context of autonomous driving shouldn’t miss Patrick Langechuan Liu’s comprehensive “crash course” on the subject.
