16.7.4.2.6 Surveys, Evaluation, Datasets, Human Detection, People Detection, Pedestrians

Chapter Contents (Back)
Human Detection. Evaluation, Pedestrian Detection. See also Human Detection, People Detection, Pedestrians, Locating. See also Tracking People, Human Tracking, Pedestrian Tracking.

Daimler Pedestrian Detection Benchmark,
2009.
HTML Version. Dataset, Pedestrian Detection. Dataset, Surveillance. See also Daimler. Training set: 15,560 pedestrian and non-pedestrian samples. 6744 additional images. Test set: a sequence with more than 21,790 images with 56,492 pedestrian labels. From a vehicle in 27 minutes of urban driving. VGA resolution. Dataset used in: See also Monocular Pedestrian Detection: Survey and Experiments. 0906

Edinburgh Informatics Forum Pedestrian Database,
2010.
WWW Link. Dataset, Human Tracking. Dataset, Surveillance. Overhead views, of a building atrium. Several months of observations, with trajectories (computed). 1007

Dalal, N.[Navneet],
INRIA Person Dataset,
Online2005
WWW Link. Dataset, Human Motion. The collected dataset for the above paper, from various sources. BibRef 0500

Enzweiler, M.[Markus], Gavrila, D.M.[Dariu M.],
Monocular Pedestrian Detection: Survey and Experiments,
PAMI(31), No. 12, December 2009, pp. 2179-2195.
IEEE DOI 0911
Survey, Pedestrian Detection. See also Daimler Pedestrian Detection Benchmark. wavelet-based AdaBoost cascade ( See also Detecting Pedestrians Using Patterns of Motion and Appearance. ), HOG/linSVM ( See also Histograms of Oriented Gradients for Human Detection. ), NN/LRF ( See also Adaptable Time-Delay Neural Network Algorithm for Image Sequence Analysis, An. ), and combined shape-texture detection ( See also Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle. ) BibRef

Gandhi, T.[Tarak], Trivedi, M.M.[Mohan Manubhai],
Pedestrian Protection Systems: Issues, Survey, and Challenges,
ITS(8), No. 3, September 2007, pp. 413-430.
IEEE DOI 0710
Survey, Pedestrian Detection. BibRef

Paisitkriangkrai, S.[Sakrapee], Shen, C.H.[Chun-Hua], Zhang, J.[Jian],
Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features,
CirSysVideo(18), No. 8, August 2008, pp. 1140-1151.
IEEE DOI 0809
BibRef
And:
Real-time Pedestrian Detection Using a Boosted Multi-layer Classifier,
VS08(xx-yy). 0810
BibRef

Paisitkriangkrai, S.[Sakrapee], Shen, C.H.[Chun-Hua], Zhang, J.[Jian],
Performance evaluation of local features in human classification and detection,
IET-CV(2), No. 4, December 2008, pp. 236-246.
DOI Link 0905
BibRef

Wu, Y.[Yang], Liu, Y.L.[Yuan-Liu], Yuan, Z.J.[Ze-Jian], Zheng, N.N.[Nan-Ning],
IAIR-CarPed: A psychophysically annotated dataset with fine-grained and layered semantic labels for object recognition,
PRL(33), No. 2, 15 January 2012, pp. 218-226.
Elsevier DOI 1112
Dataset, Pedestrian Detection. Object recognition; Image database; Object detection; Pedestrian detection; Psychophysical experiments BibRef

Xu, J., Wu, Q., Zhang, J., Tang, Z.,
Fast and Accurate Human Detection Using a Cascade of Boosted MS-LBP Features,
SPLetters(19), No. 10, October 2012, pp. 676-679.
IEEE DOI 1209
BibRef

Hussein, M.[Mohamed], Porikli, F.M.[Fatih M.], Davis, L.S.[Larry S.],
A Comprehensive Evaluation Framework and a Comparative Study for Human Detectors,
ITS(10), No. 3, September 2009, pp. 417-427.
IEEE DOI 0909
BibRef

Joshi, A.J.[Ajay J.], Porikli, F.M.[Fatih M.],
Scene-Adaptive Human Detection with Incremental Active Learning,
ICPR10(2760-2763).
IEEE DOI 1008
BibRef

Oberli, C., Torres-Torriti, M., Landau, D.,
Performance Evaluation of UHF RFID Technologies for Real-Time Passenger Recognition in Intelligent Public Transportation Systems,
ITS(11), No. 3, September 2010, pp. 748-753.
IEEE DOI 1003
See also Crowded pedestrian counting at bus stops from perspective transformations of foreground areas. BibRef

García-Martín, Á.[Álvaro], Martínez, J.M.[José M.], Bescós, J.[Jesús],
A corpus for benchmarking of people detection algorithms,
PRL(33), No. 2, 15 January 2012, pp. 152-156.
Elsevier DOI 1112
Dataset, Person Detection. People detection; Ground-truth; Corpus; Dataset; Surveillance video BibRef

Dollar, P.[Piotr], Wojek, C.[Christian], Schiele, B.[Bernt], Perona, P.[Pietro],
Pedestrian Detection: An Evaluation of the State of the Art,
PAMI(34), No. 4, April 2012, pp. 743-761.
IEEE DOI 1203
BibRef
Earlier:
Pedestrian detection: A benchmark,
CVPR09(304-311).
IEEE DOI 0906
Evaluation, Pedestrian Detection. Dataset, methodology. 16 detectors over 6 datasets. Results poor for low resolution and occlusions. BibRef

Dollar, P.[Piotr], Belongie, S.J.[Serge J.], Perona, P.[Pietro],
The Fastest Pedestrian Detector in the West,
BMVC10(xx-yy).
HTML Version. 1009
BibRef

Villamizar, M.[Michael], Andrade-Cetto, J.[Juan], Sanfeliu, A.[Alberto], Moreno-Noguer, F.[Francesc],
Bootstrapping Boosted Random Ferns for discriminative and efficient object classification,
PR(45), No. 9, September 2012, pp. 3141-3153.
Elsevier DOI 1206
BibRef
Earlier: A1, A4, A2, A3:
Detection Performance Evaluation of Boosted Random Ferns,
IbPRIA11(67-75).
Springer DOI 1106
BibRef
Earlier:
Shared Random Ferns for Efficient Detection of Multiple Categories,
ICPR10(388-391).
IEEE DOI 1008
BibRef
And:
Efficient rotation invariant object detection using boosted Random Ferns,
CVPR10(1038-1045).
IEEE DOI 1006
Object detection; Boosting; Bootstrapping; Random Ferns BibRef

Villamizar, M.[Michael], Garrell, A.[Anaís], Sanfeliu, A.[Alberto], Moreno-Noguer, F.[Francesc],
Interactive multiple object learning with scanty human supervision,
CVIU(149), No. 1, 2016, pp. 51-64.
Elsevier DOI 1606
BibRef
Earlier:
Online human-assisted learning using Random Ferns,
ICPR12(2821-2824).
WWW Link. 1302
Object recognition BibRef

Villamizar, M.[Michael], Grabner, H.[Helmut], Moreno-Noguer, F.[Francesc], Andrade-Cetto, J.[Juan], Van Gool, L.J.[Luc J.], Sanfeliu, A.[Alberto],
Efficient 3D Object Detection using Multiple Pose-Specific Classifiers,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Villamizar, M.[Michael], Sanfeliu, A.[Alberto], Andrade-Cetto, J.[Juan],
Local Boosted Features for Pedestrian Detection,
IbPRIA09(128-135).
Springer DOI 0906
BibRef
Earlier:
Unidimensional Multiscale Local Features for Object Detection Under Rotation and Mild Occlusions,
IbPRIA07(II: 645-651).
Springer DOI 0706
BibRef
Earlier:
Orientation Invariant Features for Multiclass Object Recognition,
CIARP06(655-664).
Springer DOI 0611
BibRef
Earlier:
Computation of Rotation Local Invariant Features using the Integral Image for Real Time Object Detection,
ICPR06(IV: 81-85).
IEEE DOI 0609
BibRef

Simonnet, D., Velastin, S.A., Turkbeyler, E., Orwell, J.,
Backgroundless detection of pedestrians in cluttered conditions based on monocular images: a review,
IET-CV(6), No. 6, 2012, pp. 540-550.
DOI Link 1301
Survey, Pedestrian Detection. BibRef

Garci´a-Marti´n, A., Marti´nez, J.M.,
People detection in surveillance: classification and evaluation,
IET-CV(9), No. 5, 2015, pp. 779-788.
DOI Link 1511
image classification BibRef

Winterlich, A.[Anthony], Hughes, C.[Ciarán], Kilmartin, L.[Liam], Glavin, M.[Martin], Jones, E.[Edward],
An oriented gradient based image quality metric for pedestrian detection performance evaluation,
SP:IC(31), No. 1, 2015, pp. 61-75.
Elsevier DOI 1502
Image quality BibRef

Hosang, J.[Jan], Benenson, R.[Rodrigo], Dollár, P., Schiele, B.[Bernt],
What Makes for Effective Detection Proposals?,
PAMI(38), No. 4, April 2016, pp. 814-830.
IEEE DOI 1603
Detectors BibRef

Benenson, R.[Rodrigo], Omran, M.[Mohamed], Hosang, J.[Jan], Schiele, B.[Bernt],
Ten Years of Pedestrian Detection, What Have We Learned?,
CVRoads14(613-627).
Springer DOI 1504
Survey, Pedestrian Detection. BibRef

Zhang, S., Benenson, R.[Rodrigo], Omran, M.[Mohamed], Hosang, J.[Jan], Schiele, B.[Bernt],
How Far are We from Solving Pedestrian Detection?,
CVPR16(1259-1267)
IEEE DOI 1612
BibRef


Swetha, S.[Sirnam], Mishra, A.[Anand], Hegde, G.M.[Guruprasad M.], Jawahar, C.V.,
Efficient object annotation for surveillance and automotive applications,
CVAST16(1-6)
IEEE DOI 1606
computer vision. Annotation for pedestrians. BibRef

Yuan, Y.[Yuan], Lin, W.S.[Wei-Si], Fang, Y.M.[Yu-Ming],
Is pedestrian detection robust for surveillance?,
ICIP15(2776-2780)
IEEE DOI 1512
BibRef

Ninomiya, H.[Hiroki], Ohki, H.[Hidehiro], Gyohten, K.[Keiji], Sueda, N.[Naomichi],
An evaluation on robustness and brittleness of HOG features of human detection,
FCV11(1-5).
IEEE DOI 1102
BibRef

Tosato, D.[Diego], Farenzena, M.[Michela], Cistani, M.[Marco], Murino, V.[Vittorio],
A Re-evaluation of Pedestrian Detection on Riemannian Manifolds,
ICPR10(3308-3311).
IEEE DOI 1008
See also Multi-class Classification on Riemannian Manifolds for Video Surveillance. BibRef

Westall, P., Ford, J.J., O'Shea, P., Hrabar, S.,
Evaluation of Maritime Vision Techniques for Aerial Search of Humans in Maritime Environments,
DICTA08(176-183).
IEEE DOI 0812
BibRef

Bertozzi, M., Broggi, A., Grisleri, P., Tibaldi, A., del Rose, M.,
A tool for vision based pedestrian detection performance evaluation,
IVS04(784-789).
WWW Link. 0411
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Counting People, Transportation System Monitoring, Queues .


Last update:May 25, 2017 at 22:18:08