thedash / deep-learning-for-tracking-and-detection Goto Github PK

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

• Region Proposal
  • Scalable Object Detection Using Deep Neural Networks (cvpr14) (pdf, notes)
  • Selective Search for Object Recognition (ijcv2013) (pdf, notes)
• RCNN
  • Faster R-CNN Towards Real-Time Object Detection with Region Proposal Networks (tpami17) (pdf, notes)
  • RFCN - Object Detection via Region-based Fully Convolutional Networks (nips16) (pdf, notes) [Microsoft Research]
  • Mask R-CNN (iccv17) (pdf, (notes, arxiv, code (keras), code (tensorflow)) [Facebook AI Research]
• 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)
• 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)
• Misc
  • OverFeat Integrated Recognition, Localization and Detection using Convolutional Networks (ax1402/iclr14) (pdf, notes)
  • LSDA Large scale detection through adaptation (ax1411/nips14) (pdf, notes)

• 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) (pdf, arxiv, code) [Microsoft Research]
  • 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)

• Deep Learning
  • Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies (ax1704/iccv17) (Stanford) (pdf, arxiv, project page, notes)
• Reinforcement Learning
  • Learning to Track: Online Multi-object Tracking by Decision Making (iccv15) (Stanford) (pdf, code (Matlab), project page, notes)
• Network Flow
  • Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor (iccv15) (NEC Labs) (pdf, author page, notes)
  • Deep Network Flow for Multi-Object Tracking (cvpr17) (NEC Labs) (pdf, supplementary, notes)
• Graph Optimization
  • A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects (arxiv July 2016) (highest MT on MOT2015) (University of Freiburg, Germany) (pdf, arxiv, author page, notes)
• Baseline
  • Simple Online and Realtime Tracking (icip16) (pdf, notes, code)
  • High-Speed Tracking-by-Detection Without Using Image Information (avss17) (pdf, notes, code)

• Reinforcement Learning
  • Deep Reinforcement Learning for Visual Object Tracking in Videos (arxiv April 2017) (USC-Santa Barbara, Samsung Research) (pdf, arxiv, author page, notes)
  • Visual Tracking by Reinforced Decision Making (arxiv February 2017) (Seoul National University, Chung-Ang University) (pdf, arxiv, author page, notes)
  • Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning (cvpr17) (Seoul National University) (pdf, supplementary, project page, notes)
  • End-to-end Active Object Tracking via Reinforcement Learning (arxiv 30 May 2017) (Peking University, Tencent AI Lab) (pdf, arxiv)
• Siamese
  • Fully-Convolutional Siamese Networks for Object Tracking [eccv16] [pdf] [project] [notes]
  • High Performance Visual Tracking with Siamese Region Proposal Network [cvpr18] [pdf] [author] [notes]

• Do Deep Nets Really Need to be Deep (NIPS 2014) (pdf, notes)
• Synthetic Gradients
  • Decoupled Neural Interfaces using Synthetic Gradients (arxiv August 2016) (pdf, notes)
  • Understanding Synthetic Gradients and Decoupled Neural Interfaces (arxiv March 2017) (pdf, notes)

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

• Video Frame Interpolation via Adaptive Convolution (cvpr17 / iccv17) (pdf (cvpr17), (pdf (iccv17), ppt)

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

• Multi Object Tracking
  • IDOT
  • UA-DETRAC Benchmark Suite
  • GRAM Road-Traffic Monitoring
  • Stanford Drone Dataset
  • Ko-PER Intersection Dataset
  • TRANCOS
  • Urban Tracker
  • DARPA VIVID / PETS 2005 (Non stationary camera)
  • KIT-AKS (No ground truth)
  • CBCL StreetScenes Challenge Framework (No top down viewpoint)
  • MOT 2015 (mostly street level camera viewpoint)
  • MOT 2016 (mostly street level camera viewpoint)
  • MOT 2017 (mostly street level camera viewpoint)
  • PETS 2009 (No vehicles)
  • PETS 2017 (Low density; mostly pedestrians)
  • KITTI Tracking Dataset (No top down viewpoint; non stationary camera)
  • The WILDTRACK Seven-Camera HD Dataset (pedestrian detection and tracking)
  • 3D Traffic Scene Understanding from Movable Platforms (intersection traffic/stereo setup/moving camera)
• Video Understanding / Activity Recognition
  • YouTube-8M
  • AVA: A Video Dataset of Atomic Visual Action
  • VIRAT Video Dataset
• Video Detection
  • YouTube-BB
• Static Detection
  • Object Detection-based annotations for some frames of the VIRAT dataset
• Static Segmentation
  • COCO - Common Objects in Context
  • UC Berkeley Computer Vision Group - Contour Detection and Image Segmentation
• Video Segmentation
  • DAVIS: Densely Annotated VIdeo Segmentation
• Classification
  • ImageNet Large Scale Visual Recognition Competition 2012
  • Animals with Attributes 2
  • CompCars Dataset
• Optical Flow
  • Middlebury
  • MPI Sintel

• Datasets
  • List of traffic surveillance datasets
• Single Object Tracking
  • List of deep learning based tracking papers
  • List of single object trackers with results on OTB
  • Collection of Correlation Filter based trackers with links to papers, codes, etc
  • VOT2018 Trackers repository
• Multi Object Tracking
  • List of multi object tracking papers
  • A collection of Multiple Object Tracking (MOT) papers in recent years, with notes
• Deep Compressed Sensing
  • Reproducible Deep Compressive Sensing
• Misc
  • List of Matlab frameworks, libraries and software
  • Face Recognition

• Static Detection
  • Deep Learning for Object Detection: A Comprehensive Review
  • Review of Deep Learning Algorithms for Object Detection
  • A Simple Guide to the Versions of the Inception Network
  • R-CNN, Fast R-CNN, Faster R-CNN, YOLO - Object Detection Algorithms
  • A gentle guide to deep learning object detection
  • The intuition behind RetinaNet
  • YOLO—You only look once, real time object detection explained
  • Understanding Feature Pyramid Networks for object detection (FPN)
  • Fast object detection with SqueezeDet on Keras
  • Region of interest pooling explained
• Video Detection
  • How Microsoft Does Video Object Detection - Unifying the Best Techniques in Video Object Detection Architectures in a Single Model
• Deep RL
  • Deep Reinforcement Learning: Pong from Pixels
  • Demystifying Deep Reinforcement Learning
• Autoencoders
  • Guide to Autoencoders
  • Applied Deep Learning - Part 3: Autoencoders
  • Denoising Autoencoders
  • Stacked Denoising Autoencoders
  • A Gentle Introduction to LSTM Autoencoders
  • Variational Autoencoder in TensorFlow
  • Variational Autoencoders with Tensorflow Probability Layers

• Multi Object Tracking
  • Globally-optimal greedy algorithms for tracking a variable number of objects [cvpr11] [matlab] [author]
  • Continuous Energy Minimization for Multitarget Tracking [cvpr11 / iccv11 / tpami 2014] [matlab]
  • Discrete-Continuous Energy Minimization for Multi-Target Tracking [cvpr12] [matlab] [project]
  • The way they move: Tracking multiple targets with similar appearance [iccv13] [matlab]
  • 3D Traffic Scene Understanding from Movable Platforms [2d_tracking] [pami14/kit13/iccv13/nips11] [C++/matlab]
  • Multiple target tracking based on undirected hierarchical relation hypergraph [cvpr14] [C++] [author]
  • Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning [cvpr14] [matlab] (project)
  • Learning to Track: Online Multi-Object Tracking by Decision Making [iccv15] [matlab]
  • Joint Tracking and Segmentation of Multiple Targets [cvpr15] [matlab]
  • Multiple Hypothesis Tracking Revisited [iccv15] [highest MT on MOT2015 among open source trackers] [matlab]
  • Simple Online and Realtime Tracking [icip 2016] [python]
  • Deep SORT : Simple Online Realtime Tracking with a Deep Association Metric [icip 2017] [python]
  • Combined Image- and World-Space Tracking in Traffic Scenes [icra 2017] [c++]
  • High-Speed Tracking-by-Detection Without Using Image Information [avss 2017] [python]
• Single Object Tracking
  • A collection of common tracking algorithms (2003-2012) [c++/matlab]
  • SenseTime Research platform for single object tracking, implementing algorithms like SiamRPN and SiamMask [pytorch]
  • In Defense of Color-based Model-free Tracking [cvpr15] [c++]
  • Hierarchical Convolutional Features for Visual Tracking [iccv15] [matlab]
  • Visual Tracking with Fully Convolutional Networks [iccv15] [matlab]
  • DeepTracking: Seeing Beyond Seeing Using Recurrent Neural Networks [aaai 2016] [torch 7]
  • Learning Multi-Domain Convolutional Neural Networks for Visual Tracking [cvpr16] [vot2015 winner] [matlab/matconvnet]
  • Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking [eccv 2016] [matlab]
  • Fully-Convolutional Siamese Networks for Object Tracking [eccvw 2016] [matlab/matconvnet] [project]
  • DCFNet: Discriminant Correlation Filters Network for Visual Tracking [arxiv1704] [matlab/matconvnet] [pytorch]
  • End-to-end representation learning for Correlation Filter based tracking [cvpr17] [matlab/matconvnet] [tensorflow/inference_only] [project]
  • RATM: Recurrent Attentive Tracking Model [cvprw17] [python]
  • ROLO : Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking [iscas 2017] [tensorfow]
  • ECO: Efficient Convolution Operators for Tracking [cvpr17] [matlab]
  • Detect to Track and Track to Detect [iccv17] [matlab]
  • High Performance Visual Tracking with Siamese Region Proposal Network [cvpr18] [pytorch] [pytorch/reimplementation]
  • Distractor-aware Siamese Networks for Visual Object Tracking [eccv18] [vot18 winner] [pytorch]
  • Fast Online Object Tracking and Segmentation: A Unifying Approach [cvpr19] [pytorch] [project]
• Video Detection
  • Flow-Guided Feature Aggregation for Video Object Detection [nips 2016 / iccv17] [python/cuda]
  • T-CNN: Tubelets with Convolution Neural Networks [cvpr16] [python]
  • TPN: Tubelet Proposal Network [cvpr17] [python]
  • Mobile Video Object Detection with Temporally-Aware Feature Maps [cvpr18] [Google] [tensorflow]
• Static Detection and Matching
  • Frameworks
    • Tensorflow object detection API
    • Detectron
    • Open MMLab Detection Toolbox with PyTorch
  • Region Proposal
    • MCG : Multiscale Combinatorial Grouping - Object Proposals and Segmentation (project) [tpami16/cvpr14] [python]
    • COB : Convolutional Oriented Boundaries (project) [tpami18/eccv16] [matlab/caffe]
  • FPN
    • Feature Pyramid Networks for Object Detection [caffe/python]
  • RCNN
    • RFCN (author) [caffe/matlab]
    • RFCN-tensorflow [tensorflow]
    • PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
    • Mask R-CNN - TensorFlow, Keras
    • Light-head R-CNN [cvpr18] [TensorFlow]
    • Evolving Boxes for Fast Vehicle Detection [icme18] [Caffe/Python]
    • Cascade R-CNN (cvpr18) - Detectron, Caffe
  • SSD
    • SSD-Tensorflow [tensorflow]
    • SSD-Tensorflow (tf.estimator) [tensorflow]
    • SSD-Tensorflow (tf.slim) [tensorflow]
    • SSD-Keras [keras]
    • SSD-Pytorch [pytorch]
    • Enhanced SSD with Feature Fusion and Visual Reasoning [NCA18] [TensorFlow]
    • RefineDet - Single-Shot Refinement Neural Network for Object Detection [cvpr18] [caffe]
  • YOLO
    • Darknet: Convolutional Neural Networks [c/python]
    • YOLO9000: Better, Faster, Stronger - Real-Time Object Detection. 9000 classes! [c/python]
    • Darkflow [tensorflow]
    • Pytorch Yolov2 [pytorch]
    • Yolo-v3 and Yolo-v2 for Windows and Linux [c/python]
    • YOLOv3 in PyTorch [pytorch]
    • pytorch-yolo-v3 [pytorch] [no training] [tutorial]
    • YOLOv3_TensorFlow [tensorflow]
    • tensorflow-yolo-v3 [tensorflow slim]
    • tensorflow-yolov3 [tensorflow slim]
    • keras-yolov3 [keras]
  • Relation Networks for Object Detection [cvpr18] [MXNet]
  • DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling [iccv17(poster)] [theano]
  • SNIPER: Efficient Multi-Scale Training [cvpr18 / nips18] [mxnet]
  • Multi-scale Location-aware Kernel Representation for Object Detection [cvpr18] [caffe/python]
  • Matching
    • Matchnet
    • Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
  • Boundary Detection
    • Holistically-Nested Edge Detection (HED) (iccv15) [caffe]
    • Edge-Detection-using-Deep-Learning (HED) [tensorflow]
    • Crisp Boundary Detection Using Pointwise Mutual Information (eccv14) [matlab]
• Optical Flow
  • FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks (cvpr17) - caffe, pytorch/nvidia
  • SPyNet: Spatial Pyramid Network for Optical Flow (cvpr17) - lua, pytorch
  • Guided Optical Flow Learning (cvprw17) - caffe, tensorflow
  • Fast Optical Flow using Dense Inverse Search (DIS) [eccv16] [C++]
  • A Filter Formulation for Computing Real Time Optical Flow [ral16] [c++/cuda - matlab,python wrappers]
  • PatchBatch - a Batch Augmented Loss for Optical Flow [cvpr16] [python/theano]
  • Piecewise Rigid Scene Flow [iccv13/eccv14/ijcv15] [c++/matlab]
  • DeepFlow v2 (iccv13) - c++/python/matlab, project
  • An Evaluation of Data Costs for Optical Flow [gcpr13] [matlab]
• Instance Segmentation
  • Fully Convolutional Instance-aware Semantic Segmentation [cvpr17] [coco16 winner] [mxnet]
• Autoencoders
  • β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework [iclr 2017] [deepmind] [tensorflow] [tensorflow] [pytorch]
  • Disentangling by Factorising [arxiv 2018/06] [pytorch]
• Classification
  • Learning Efficient Convolutional Networks Through Network Slimming [iccv17] [pytorch]
• Deep RL
  • Asynchronous Methods for Deep Reinforcement Learning
• Misc
  • Deformable Convolutional Networks
  • RNNexp