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Denis Rozumny
เข้าร่วมเมื่อ 24 ก.ค. 2015
Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objects (CVPR 2020)
We propose a novel method that tracks fast moving objects, mainly non-uniform spherical, in full 6 degrees of freedom, estimating simultaneously their 3D motion trajectory, 3D pose and object appearance changes with a time step that is a fraction of the video frame exposure time. The sub-frame object localization and appearance estimation allows realistic temporal super-resolution and precise shape estimation. The method, called TbD-3D (Tracking by Deblatting in 3D) relies on a novel reconstruction algorithm which solves a piece-wise deblurring and matting problem. The 3D rotation is estimated by minimizing the reprojection error. As a second contribution, we present a new challenging dataset with fast moving objects that change their appearance and distance to the camera. High speed camera recordings with zero lag between frame exposures were used to generate videos with different frame rates annotated with ground-truth trajectory and pose.
arxiv.org/abs/1911.10927
arxiv.org/abs/1911.10927
มุมมอง: 46
วีดีโอ
Tracking by 3D Model Estimation of Unknown Objects in Videos (ICCV 2023)
มุมมอง 255ปีที่แล้ว
A short video summarizing the main ideas of the tracking-by-3D method. Accepted to CVPR ICCV 2023. arXiv: arxiv.org/abs/2304.06419 Open-source PyTorch implementation: github.com/rozumden/tracking-by-3d Abstract: Most model-free visual object tracking methods formulate the tracking task as object location estimation given by a 2D segmentation or a bounding box in each video frame. We argue that ...
[CVPR 2022] Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos
มุมมอง 8942 ปีที่แล้ว
We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video. To this end, we model the blurred appearance of a fast moving object in a generative fashion by parametrizing its 3D position, rotation, velocity, acceleration, bounces, shape, and texture over the duration of a predefined time window spanning multiple frames. Using ...
Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects
มุมมอง 2.6K3 ปีที่แล้ว
We address the novel task of jointly reconstructing the 3D shape, texture, and motion of an object from a single motion-blurred image and its background. The full paper is available on arXiv: arxiv.org/abs/2012.00595
DeFMO: Deblurring and Shape Recovery of Fast Moving Objects (CVPR 2021)
มุมมอง 3.9K3 ปีที่แล้ว
A short video summarizing the main ideas of the DeFMO method. Accepted to CVPR 2021. arXiv: arxiv.org/abs/2012.00595 Open-source PyTorch implementation: github.com/rozumden/DeFMO Abstract: Objects moving at high speed appear significantly blurred when captured with cameras. The blurry appearance is especially ambiguous when the object has complex shape or texture. In such cases, classical metho...
Kakkos Beach Hotel Through Snapchat Spectacles
มุมมอง 3.9K4 ปีที่แล้ว
Kakkos Beach Hotel, Koutsounari/Ferma/Ierapetra, Crete, Greece. The video also features Kakkos Bay Hotel, the Waterfall of Milona, Chrissi island and Minoan Water Tanks. We spent one week there in July 2020. Recorded using Spectacles by Snapchat.
Demo of Fast Moving Objects Detection
มุมมอง 2.4K7 ปีที่แล้ว
For more details visit our webpage cmp.felk.cvut.cz/fmo/
Sparks from circular saw detection and tracking
มุมมอง 2177 ปีที่แล้ว
The method is described in Rozumnyi et al. “The World of Fast Moving Objects”, CVPR 2017
Hailstorm detection and tracking
มุมมอง 1317 ปีที่แล้ว
The method is described in Rozumnyi et al. “The World of Fast Moving Objects”, CVPR 2017
Fast moving object detection and tracking for table tennis
มุมมอง 5K7 ปีที่แล้ว
The method is described in Rozumnyi et al. “The World of Fast Moving Objects”, CVPR 2017
Temporal super-resolution for table tennis
มุมมอง 4547 ปีที่แล้ว
The method is described in Rozumnyi et al. “The World of Fast Moving Objects”, CVPR 2017
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Цікаво
What’s the point of this video?
How can I Contact you,for project purpose
Jadę tam za tydzień 😊
Nice work cant wait to try some of the projects you shared. Thank you!
Красота
doubt you will see this but im making a auto foseball goalie and your videos on fast moving objects seem interesting, would love to see the code behind this but im not counting on it. For not im probley just going to use Open-Cv
Thank you for your comment! The code is open-sourced on GitHub: github.com/rozumden/fmo-cpp-demo
Cudowne miejsce
Wow. You have my head spinning now. You have given me ideas on how I can maybe use a tiny bit of your concepts to help my motion tracking of lasers in a laser shooting game I am trying to make, using a camera to track the laser. It's like a mix of openCV and predictive image reconstruction techniques. Cool stuff.
Amazing implementation, assuming this is live video processing and adding the graphics in real time, and since that is kind of the whole point of your video series of fast object tracking, I am sure it is. I am trying to make a computer game using 2 laser pointers, one red, one green, to track the shots of 2 different shooters, in a shooting game. It is difficult to deal with as the lasers create a big halo, or look white on the camera, and the Full HD 30 FPS webcam I am using seems very slow. I don't know if I will get there. Your videos look amazing. This ping pong video is your best video, but the one that is the most fun is the ball path reconstruction video. You could do stuff like Dude Perfect does. I recently added object tracking to my CNC Controller software, NewRAD CNC. I have 5 cameras on my CNC. And now my CNC software controller can show up to 3 cameras on the screen at the same time. Two of the cameras are microscope cameras, and they let me find a drill hole or other reference point automatically using OpenCV. I am actually using the AForge OpenCV wrapper library for C#, which is part of the reason it is slower than your nicer C++ apps. Here's a video of my app automatically locating a drill hole using 2 microscope cameras, 1 for the X axis, and 1 for the Y axis. th-cam.com/video/cICAZHzGLwo/w-d-xo.html
I have tried to use pretrained model on image but its give error, '' bbox = extend_bbox(bbox.copy(),4*np.max(radius),g_resolution_y/g_resolution_x,I.shape) File "C:\Users\haide\Desktop\DeFMO-master\utils.py", line 203, in extend_bbox height, width = bbox[2] - bbox[0], bbox[3] - bbox[1] IndexError: list index out of range '' what could be the cause of this
I have to find obstacles infront of train driver eye view camera. Can you provide any helping material related to that?
Hi, you can check some of our recent publications here: people.inf.ethz.ch/denysr/
can you plz share source code and camera specification
Of course. The code is open-sourced here [ github.com/rozumden/fmo-cpp-demo ]. You can download the Android app here [ cmp.felk.cvut.cz/fmo/app.apk ]. The camera is a basic USB web camera (Logitech USB webcam 2mp). Android app works on most mobile devices.
@Muhammad Arslan Nasr Hi, it should work. Please copy-paste it into your web browser. For some reason, TH-cam blocks clicking directly on this link.
@Muhammad Arslan Nasr You can access the Android code here: github.com/rozumden/fmo-android Detecting the cricket ball should be possible.
@@royal26teja you should put it in the same folder as the source code
Is it enough if i put fmodetect.h5, or should i generate data set too
Не хватает только озвучивания увидимых мест
Красиво! Спасибо за видео
Could you please send me your implementation to yousefaboelm3aty@gmail.com, my bachelor’s is on an ABB robot playing ping pong, this implementation would greatly help me, thank youu:)
You can use the demo implemented here: github.com/rozumden/fmo-cpp-demo
is it using background subtraction?
Programming Freak yes, it's using background subtraction in its core, but the frames are registered in each step. The background is also obtained as a median of previous frames, which is robust to small camera motions.
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