Company Filing History:
Years Active: 2025
Title: Debidatta Dwibedi: Innovator in Video Processing Technologies
Introduction
Debidatta Dwibedi is a notable inventor based in Santa Clara, CA (US). He has made significant contributions to the field of video processing, particularly in the area of periodic activity recognition. His innovative work has led to the development of techniques that enhance the understanding and analysis of video content.
Latest Patents
Dwibedi holds a patent titled "Class agnostic repetition counting in video(s) utilizing a temporal self-similarity matrix." This patent discloses techniques that enable the processing of videos capturing periodic activities using a repetition network. The technology generates periodic output, such as the period length of the activity captured in the video and frame-wise periodicity indications. Various implementations of this invention include a class agnostic repetition network, which can be utilized for a wide variety of periodic activities. Additionally, the patent discusses generating synthetic repetition videos to train the repetition network effectively. He has 1 patent to his name.
Career Highlights
Debidatta Dwibedi is currently employed at Google Inc., where he continues to push the boundaries of video processing technologies. His work at Google has allowed him to collaborate with some of the brightest minds in the industry, contributing to advancements in machine learning and artificial intelligence.
Collaborations
One of his notable coworkers is Yusuf Aytar, with whom he has likely shared insights and expertise in their respective fields. Their collaboration exemplifies the spirit of innovation and teamwork that drives progress in technology.
Conclusion
Debidatta Dwibedi's contributions to video processing technologies highlight his role as an innovator in the field. His patent on class agnostic repetition counting showcases his ability to develop solutions that address complex challenges in video analysis. His work at Google Inc. continues to influence the landscape of video processing and machine learning.