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

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

Research Data

I use DavidRM Journal for managing my research data for its excellent hierarchical organization, cross-linking and tagging capabilities.

I make available a Journal entry export file that contains tagged and categorized collection of papers, articles, tutorials, code and notes about computer vision and deep learning that I have collected over the last few years.

This is what the topic cloud looks like: Alt text

It needs Jounal 8 and can be imported using following steps:

  • Import my user preferences using File -> Import -> Import User Preferences
  • Import research data using File -> Import -> Sync from The Journal Export File

Note that my user preferences must be imported before the research data for the tagged topics to work correctly.

(optional) My global options file is also provided for those interested in a dark theme and can be imported using File -> Import -> Import Global Options

Updated: 2023-11-22

Papers

Static Detection

Region Proposal

  • Scalable Object Detection Using Deep Neural Networks [cvpr14] [pdf] [notes]
  • Selective Search for Object Recognition [ijcv2013] [pdf] [notes]

RCNN

YOLO

  • You Only Look Once Unified, Real-Time Object Detection [ax1605] [pdf] [notes]
  • YOLO9000 Better, Faster, Stronger [ax1612] [pdf] [notes]
  • YOLOv3 An Incremental Improvement [ax1804] [pdf] [notes]
  • YOLOv4 Optimal Speed and Accuracy of Object Detection [ax2004] [pdf] [notes] [code]

SSD

  • SSD Single Shot MultiBox Detector [ax1612/eccv16] [pdf] [notes]
  • DSSD Deconvolutional Single Shot Detector [ax1701] [pdf] [notes]

RetinaNet

  • Feature Pyramid Networks for Object Detection [ax1704] [pdf] [notes]
  • Focal Loss for Dense Object Detection [ax180207/iccv17] [pdf] [notes]

Anchor Free

Misc

  • OverFeat Integrated Recognition, Localization and Detection using Convolutional Networks [ax1402/iclr14] [pdf] [notes]
  • LSDA Large scale detection through adaptation [ax1411/nips14] [pdf] [notes]
  • Acquisition of Localization Confidence for Accurate Object Detection [ax1807/eccv18] [pdf] [notes] [code]
  • EfficientDet: Scalable and Efficient Object Detection [cvpr20] [pdf]
  • Generalized Intersection over Union A Metric and A Loss for Bounding Box Regression [ax1902/cvpr19] [pdf] [notes] [code] [project]

Video Detection

Tubelet

  • Object Detection from Video Tubelets with Convolutional Neural Networks [cvpr16] [pdf] [notes]
  • Object Detection in Videos with Tubelet Proposal Networks [ax1704/cvpr17] [pdf] [notes]

FGFA

  • Deep Feature Flow for Video Recognition [cvpr17] [Microsoft Research] [pdf] [arxiv] [code]
  • Flow-Guided Feature Aggregation for Video Object Detection [ax1708/iccv17] [pdf] [notes]
  • Towards High Performance Video Object Detection [ax1711] [Microsoft] [pdf] [notes]

RNN

  • Online Video Object Detection using Association LSTM [iccv17] [pdf] [notes]
  • Context Matters Refining Object Detection in Video with Recurrent Neural Networks [bmvc16] [pdf] [notes]

Multi Object Tracking

Joint-Detection

Identity Embedding

Association

  • Deep Affinity Network for Multiple Object Tracking [ax1810/tpami19] [pdf] [notes] [code] [pytorch]

Deep Learning

  • Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism [ax1708/iccv17] [pdf] [arxiv] [notes]

  • Online multi-object tracking with dual matching attention networks [ax1902/eccv18] [pdf] [arxiv] [notes] [code]

  • FAMNet Joint Learning of Feature, Affinity and Multi-Dimensional Assignment for Online Multiple Object Tracking [iccv19] [pdf] [notes]

  • Exploit the Connectivity: Multi-Object Tracking with TrackletNet [ax1811/mm19] [pdf] [notes]

  • Tracking without bells and whistles [ax1903/iccv19] [pdf] [notes] [code] [pytorch]

RNN

  • Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies [ax1704/iccv17] [Stanford] [pdf] [notes] [arxiv] [project],
  • Multi-object Tracking with Neural Gating Using Bilinear LSTM [eccv18] [pdf] [notes]
  • Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking [cvpr19] [pdf] [notes] [code]

Unsupervised Learning

  • Unsupervised Person Re-identification by Deep Learning Tracklet Association [ax1809/eccv18] [pdf] [notes]
  • Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers [ax1809/cvpr19] [pdf] [arxiv] [notes] [code]
  • Simple Unsupervised Multi-Object Tracking [ax2006] [pdf] [notes]

Reinforcement Learning

Network Flow

Graph Optimization

  • A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects [ax1607] [highest MT on MOT2015] [University of Freiburg, Germany] [pdf] [arxiv] [author] [notes]

Baseline

Metrics

  • HOTA A Higher Order Metric for Evaluating Multi-object Tracking [ijcv20/08] [pdf] [notes] [code]

Single Object Tracking

Reinforcement Learning

  • Deep Reinforcement Learning for Visual Object Tracking in Videos [ax1704] [USC-Santa Barbara, Samsung Research] [pdf] [arxiv] [author] [notes]
  • Visual Tracking by Reinforced Decision Making [ax1702] [Seoul National University, Chung-Ang University] [pdf] [arxiv] [author] [notes]
  • Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning [cvpr17] [Seoul National University] [pdf] [supplementary] [project] [notes] [code]
  • End-to-end Active Object Tracking via Reinforcement Learning [ax1705] [Peking University, Tencent AI Lab] [pdf] [arxiv]

Siamese

Correlation

Misc

  • Bridging the Gap Between Detection and Tracking A Unified Approach [iccv19] [pdf] [notes]

Deep Learning

  • Do Deep Nets Really Need to be Deep [nips14] [pdf] [notes]

Synthetic Gradients

  • Decoupled Neural Interfaces using Synthetic Gradients [ax1608] [pdf] [notes]
  • Understanding Synthetic Gradients and Decoupled Neural Interfaces [ax1703] [pdf] [notes]

Efficient

  • EfficientNet:Rethinking Model Scaling for Convolutional Neural Networks [icml2019] [pdf] [notes]

Unsupervised Learning

  • Learning Features by Watching Objects Move (cvpr17) [pdf] [notes]

Interpolation

Autoencoder

Variational

  • beta-VAE Learning Basic Visual Concepts with a Constrained Variational Framework [iclr17] [pdf] [notes]
  • Disentangling by Factorising [ax1806] [pdf] [notes]

Datasets

Multi Object Tracking

UAV

Synthetic

Microscopy / Cell Tracking

Single Object Tracking

Video Detection

Video Understanding / Activity Recognition

Static Detection

Animals

Boundary Detection

Static Segmentation

Video Segmentation

Classification

Optical Flow

Motion Prediction

Code

General Vision

Multi Object Tracking

Frameworks

General

Baseline

Siamese

Unsupervised

Re-ID

Frameworks

Graph NN

Microscopy / cell tracking

3D

Metrics

Single Object Tracking

GUI Application / Large Scale Tracking / Animals

Video Detection

Action Detection

Frameworks

Static Detection and Matching

Frameworks

Region Proposal

FPN

RCNN

SSD

RetinaNet

YOLO

Anchor Free

Misc

Matching

Boundary Detection

Text Detection

Frameworks

3D Detection

Frameworks

Optical Flow

Frameworks

Instance Segmentation

Frameworks

Semantic Segmentation

Frameworks

Polyp

Panoptic Segmentation

Video Segmentation

Panoptic Video Segmentation

Motion Prediction

Pose Estimation

Frameworks

Autoencoders

Classification

Frameworks

Deep RL

Annotation

Editing

Augmentation

Deep Learning

Class Imbalance

Few shot learning

Unsupervised learning

Collections

Datasets

Deep Learning

Static Detection

Video Detection

Single Object Tracking

Multi Object Tracking

Static Segmentation

Video Segmentation

Motion Prediction

Deep Compressed Sensing

Misc

Tutorials

Collections

Multi Object Tracking

Static Detection

Video Detection

Instance Segmentation

Deep Learning

Optimization

Class Imbalance

RNN

Deep RL

Autoencoders

Blogs

Releases

No releases published

Packages

No packages published

Languages