Proxy Virtual Worlds Virtual KITTI | Xerox Research Centre Europe
Computer Vision research Making everyday interaction with visual content simple Virtual KITTI dataset Virtual KITTI is a photo-realistic synthetic video dataset designed to learn and evaluate computer vision models for several video understanding tasks: object detection and multi-object tracking, scene-level and instance-level semantic segmentation, optical flow, and depth estimation. Virtual K
[Paper introduction] GMMCP-Tracker: Globally Optimal Generalized Max…
YOLOv4 builds upon previous YOLO models and introduces techniques like CSPDarknet53, SPP, PAN, Mosaic data augmentation, and modifications to existing methods to achieve state-of-the-art object detection speed and accuracy while being trainable on a single GPU. Experiments show that combining these techniques through a "bag of freebies" and "bag of specials" approach improves classifier and detect
![[Paper introduction] GMMCP-Tracker: Globally Optimal Generalized Max…](https://cdn-ak-scissors.b.st-hatena.com/image/square/6550579fb044084714e853212891f08e31e77e17/height=288;version=1;width=512/https%3A%2F%2Fcdn.slidesharecdn.com%2Fss_thumbnails%2Fcv-150724231756-lva1-app6892-thumbnail.jpg%3Fwidth%3D640%26height%3D640%26fit%3Dbounds)
MOT Challenge
Welcome to MOTChallenge: The Multiple Object Tracking Benchmark! In the recent past, the computer vision community has relied on several centralized benchmarks for performance evaluation of numerous tasks including object detection, pedestrian detection, 3D reconstruction, optical flow, single-object short-term tracking, and stereo estimation. Despite potential pitfalls of such benchmarks, they ha
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BMTT 2016
Identifying Players in Broadcast Sports Videos using Conditional Random Fields -- Wei-Lwun Lu
CVPR 2013 Open Access Repository
CRCV | Center for Research in Computer Vision at the University of Central Florida
CRCV | Center for Research in Computer Vision at the University of Central Florida
Global Data Association for Multiple Pedestrian Tracking
Anton Milan - Joint Tracking and Segmentation
CRCV | Center for Research in Computer Vision at the University of Central Florida
Afshin Dehghan
CRCV | Center for Research in Computer Vision at the University of Central Florida
https://www.robots.ox.ac.uk/~tvg/publications/2015/MOT_SSVM_CVPR15_V7_CamReady.pdf
Anton Milan - Joint Tracking and Segmentation
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics - EURASIP Journal on Image and Video Processing
[1603.00831] MOT16: A Benchmark for Multi-Object Tracking
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking