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Deep-Learning-for-Tracking-and-Detection: Collection of papers and other resourc ...

原作者: [db:作者] 来自: 网络 收藏 邀请

开源软件名称:

Deep-Learning-for-Tracking-and-Detection

开源软件地址:

https://gitee.com/jlxing/Deep-Learning-for-Tracking-and-Detection

开源软件介绍:

Collection of papers and other resources for object detection and tracking using deep learning

Object Detection

  • Mask R-CNN (pdf, arxiv, github) by Facebook AI Research!
    • Summary goes here...
  • Tensorflow object detection API: https://github.com/tensorflow/models/tree/master/object_detection. Only the two SSD nets can run at 12.5 FPS on one GTX 1080 TI (less accurate than YOLO 604x604). Next two models at 4-5 FPS (4-5% mAP better than YOLO). Best model < 1 FPS. Currently code only allow inference of 1 image at a time. Speed might improve by 2.5 times when they allow multiple image inference.

Object Tracking

  • Multi Object Tracking

    • Learning to Track: Online Multi-object Tracking by Decision Making (ICCV 2015) (Stanford) (pdf, github (Matlab), project page)

    • Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies (arxiv April 2017) (Stanford) (pdf, arxiv, project page)

    • Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor (ICCV 2015) (NEC Labs) (pdf, author page)

    • A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects (highest MT on MOT2015) (University of Freiburg, Germany) (pdf, arxiv, author page)

  • Single Object Tracking

    • Deep Reinforcement Learning for Visual Object Tracking in Videos (arxiv April 2017) (USC-Santa Barbara, Samsung Research) (pdf, arxiv, author page)

    • Visual Tracking by Reinforced Decision Making (arxiv February 2017) (Seoul National University, Chung-Ang University) (pdf, arxiv, author page)

    • Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning (CVPR 2017) (Seoul National University) (pdf, project page)

    • End-to-end Active Object Tracking via Reinforcement Learning (arxiv 30 May 2017) (Peking University, Tencent AI Lab) (pdf, arxiv

Other potentially useful papers

  • Deep Feature Flow for Video Recognition (pdf, arxiv, github) by Microsoft Research
    • Summary goes here...

Datasets

Resources

Tutorials

Code


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