Giter VIP home page Giter VIP logo

qd-track's Introduction

Quasi-Dense Instance Similarity Learning

This is the offical implementation of paper Quasi-Dense Instance Similarity learning.

We present a trailer that consists of method illustrations and tracking visualizations. Take a look!

If you have any questions or discussions, feel free to contact Jiangmiao Pang ([email protected]).

Abstract

Similarity metrics for instances have drawn much attention, due to their importance for computer vision problems such as object tracking. However, existing methods regard object similarity learning as a post-hoc stage after object detection and only use sparse ground truth matching as the training objective. This process ignores the majority of the regions on the images. In this paper, we present a simple yet effective quasi-dense matching method to learn instance similarity from hundreds of region proposals in a pair of images. In the resulting feature space, a simple nearest neighbor search can distinguish different instances without bells and whistles. When applied to joint object detection and tracking, our method can outperform existing methods without using location or motion heuristics, yielding almost 10 points higher MOTA on BDD100K and Waymo tracking datasets. Our method is also competitive on one-shot object detection, which further shows the effectiveness of quasi-dense matching for category-level metric learning.

Quasi-dense matching

teaser

Main results

With out bells and whistles, our method outperforms the states of the art on BDD100K and Waymo Tracking datasets by a large margin.

Joint object detection and tracking on BDD100K test set

mMOTA mIDF1 ID Sw.
35.2 51.8 11019

Joint object detection and tracking on Waymo validation set

Category MOTA IDF1 ID Sw.
Vehicle 55.6 66.2 24309
Pedestrian 50.3 58.4 6347
Cyclist 26.2 45.7 56
All 44.0 56.8 30712

Installation

Please refer to INSTALL.md for installation instructions.

Usages

Please refer to GET_STARTED.md for dataset preparation and running instructions.

Citation

@article{pang2020quasi,
  title={Quasi-Dense Instance Similarity Learning},
  author={Pang, Jiangmiao and Qiu, Linlu and Chen, Haofeng and Li, Qi and Darrell, Trevor and Yu, Fisher},
  journal={arXiv preprint arXiv:2006.06664},
  year={2020}
}

qd-track's People

Contributors

oceanpang avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.