What’s New in Laptop Imaginative and prescient and Object Detection? | by TDS Editors | Jul, 2024

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Earlier than we get into this week’s choice of stellar articles, we’d wish to take a second to thank all our readers, authors, and members of our broader group for serving to us attain a significant milestone, as our followers rely on Medium simply reached…

We couldn’t be extra thrilled — and grateful for everybody that has supported us in making TDS the thriving, learning-focused publication it’s. Right here’s to extra development and exploration sooner or later!

Again to our common enterprise, we’ve chosen three latest articles as our highlights this week, targeted on cutting-edge instruments and approaches from the ever-exciting fields of laptop imaginative and prescient and object detection. As multimodal fashions develop their footprint and use instances like autonomous driving, healthcare, and agriculture go mainstream, it’s by no means been extra essential for knowledge and ML practitioners to remain up-to-speed with the most recent developments. (When you’re extra desirous about different subjects in the intervening time, we’ve bought you coated! Scroll down for a handful of fastidiously picked suggestions on neuroscience, music and AI, environmentally acutely aware ML workflows, and extra.)

• Mastering Object Counting in Videos
  Correct object detection in movies comes with a number of recent challenges when in comparison with the identical course of in static photos. Lihi Gur Arie, PhD presents a transparent and concise tutorial that reveals how one can nonetheless accomplish it, and makes use of the enjoyable instance of counting transferring ants on a tree to make her case.
• Spicing Up Ice Hockey with AI: Player Tracking with Computer Vision
  For anybody searching for an intensive and fascinating challenge walkthrough, we strongly suggest Raul Vizcarra Chirinos’ writeup of his latest try to construct a hockey-player tracker from (roughly) scratch. Utilizing PyTorch, laptop imaginative and prescient strategies, and a convolutional neural community (CNN), Raul developed a prototype that may observe gamers and accumulate primary efficiency statistics.
• A Crash Course of Planning for Perception Engineers in Autonomous Driving
  Whereas we’d nonetheless be years away from self-driving automobiles dominating our roads, researchers and trade gamers have made important progress in recent times. Practitioners who’d wish to broaden their data of planning and decision-making within the context of autonomous driving shouldn’t miss Patrick Langechuan Liu’s complete “crash course” on the subject.