Object Detection

• Papers
  • R-CNN
  • MultiBox
  • SPP-Net
  • DeepID-Net
  • Fast R-CNN
  • DeepBox
  • MR-CNN
  • Faster R-CNN
  • YOLO
  • DenseBox
  • SSD
  • Inside-Outside Net
  • G-CNN
• Specific Object Deteciton
• Tutorials
• Codes
• Blogs

Papers

Deep Neural Networks for Object Detection

• paper: http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

• intro: A deep version of the sliding window method, predicts bounding box directly from each location of the topmost feature map after knowing the confidences of the underlying object categories.
• arXiv: http://arxiv.org/abs/1312.6229
• code: https://github.com/sermanet/OverFeat
• code: http://cilvr.nyu.edu/doku.php?id=software:overfeat:start

R-CNN

Rich feature hierarchies for accurate object detection and semantic segmentation(R-CNN)

• arXiv: http://arxiv.org/abs/1311.2524
• slides: http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf
• slides: http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf
• code: https://github.com/rbgirshick/rcnn
• notes: http://zhangliliang.com/2014/07/23/paper-note-rcnn/
• caffe-pr(“Make R-CNN the Caffe detection example”): https://github.com/BVLC/caffe/pull/482

MultiBox

Scalable Object Detection using Deep Neural Networks (MultiBox)

• intro: Train a CNN to predict Region of Interest.
• arXiv: http://arxiv.org/abs/1312.2249
• code: https://github.com/google/multibox

SPP-Net

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

• arXiv: http://arxiv.org/abs/1406.4729
• code: https://github.com/ShaoqingRen/SPP_net
• notes: http://zhangliliang.com/2014/09/13/paper-note-sppnet/

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

• arxiv: http://arxiv.org/abs/1407.5736

Scalable, High-Quality Object Detection

• arXiv: http://arxiv.org/abs/1412.1441
• code: https://github.com/google/multibox

DeepID-Net

DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection

• arXiv: http://arxiv.org/abs/1412.5661

Object Detection Networks on Convolutional Feature Maps

• arXiv: http://arxiv.org/abs/1504.06066

Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction

• arXiv: http://arxiv.org/abs/1504.03293
• slides: http://www.ytzhang.net/files/publications/2015-cvpr-det-slides.pdf
• code: https://github.com/YutingZhang/fgs-obj

Fast R-CNN

Fast R-CNN

• arXiv: http://arxiv.org/abs/1504.08083
• slides: http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf
• github: https://github.com/rbgirshick/fast-rcnn
• webcam demo: https://github.com/rbgirshick/fast-rcnn/pull/29
• notes: http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/
• notes: http://blog.csdn.net/linj_m/article/details/48930179
• github(“Train Fast-RCNN on Another Dataset”): https://github.com/zeyuanxy/fast-rcnn/tree/master/help/train

DeepBox

DeepBox: Learning Objectness with Convolutional Networks

• arXiv: http://arxiv.org/abs/1505.02146
• github: https://github.com/weichengkuo/DeepBox

MR-CNN

Object detection via a multi-region & semantic segmentation-aware CNN model (MR-CNN)

• arXiv: http://arxiv.org/abs/1505.01749
• code: “Pdf and code will appear here shortly – stay tuned”
  http://imagine.enpc.fr/~komodakn/
• notes: http://zhangliliang.com/2015/05/17/paper-note-ms-cnn/
• notes: http://blog.cvmarcher.com/posts/2015/05/17/multi-region-semantic-segmentation-aware-cnn/

Faster R-CNN

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks(NIPS 2015)

• arXiv: http://arxiv.org/abs/1506.01497
• github: https://github.com/ShaoqingRen/faster_rcnn
• github: https://github.com/rbgirshick/py-faster-rcnn

YOLO

You Only Look Once: Unified, Real-Time Object Detection(YOLO)

• intro: YOLO uses the whole topmost feature map to predict both confidences for multiple categories and bounding boxes (which are shared for these categories).
• arXiv: http://arxiv.org/abs/1506.02640
• code: http://pjreddie.com/darknet/yolo/
• github: https://github.com/pjreddie/darknet
• reddit: https://www.reddit.com/r/MachineLearning/comments/3a3m0o/realtime_object_detection_with_yolo/
• github(YOLO_tensorflow): https://github.com/gliese581gg/YOLO_tensorflow

R-CNN minus R

• arXiv: http://arxiv.org/abs/1506.06981

DenseBox

DenseBox: Unifying Landmark Localization with End to End Object Detection

• arXiv: http://arxiv.org/abs/1509.04874
• demo: http://pan.baidu.com/s/1mgoWWsS
• KITTI result: http://www.cvlibs.net/datasets/kitti/eval_object.php

SSD

SSD: Single Shot MultiBox Detector

• arXiv: http://arxiv.org/abs/1512.02325
• github: https://github.com/weiliu89/caffe/tree/ssd
• video: http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973

Inside-Outside Net

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

Detection results on VOC 2007 test:

Detection results on VOC 2012 test:

• intro: “0.8s per image on a Titan X GPU (excluding proposal generation) without two-stage bounding-box regression and 1.15s per image with it”.
• arxiv: http://arxiv.org/abs/1512.04143
• slides: http://www.seanbell.ca/tmp/ion-coco-talk-bell2015.pdf
• coco-leaderboard: http://mscoco.org/dataset/#detections-leaderboard

G-CNN

G-CNN: an Iterative Grid Based Object Detector

• arxiv: http://arxiv.org/abs/1512.07729

Learning Deep Features for Discriminative Localization

• homepage: http://cnnlocalization.csail.mit.edu/
• arxiv: http://arxiv.org/abs/1512.04150

Factors in Finetuning Deep Model for object detection

• arxiv: http://arxiv.org/abs/1601.05150

We don’t need no bounding-boxes: Training object class detectors using only human verification

• arxiv: http://arxiv.org/abs/1602.08405

Specific Object Deteciton

End-to-end people detection in crowded scenes

• arXiv: http://arxiv.org/abs/1506.04878
• code: https://github.com/Russell91/reinspect
• ipn: http://nbviewer.ipython.org/github/Russell91/ReInspect/blob/master/evaluation_reinspect.ipynb

Tutorials

Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection

• slides: http://research.microsoft.com/en-us/um/people/kahe/iccv15tutorial/iccv2015_tutorial_convolutional_feature_maps_kaiminghe.pdf

Codes

TensorBox: a simple framework for training neural networks to detect objects in images

• intro: “The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm”
• github: https://github.com/Russell91/TensorBox

Blogs

Convolutional Neural Networks for Object Detection

http://rnd.azoft.com/convolutional-neural-networks-object-detection/