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

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

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

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

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],
Boosted Random Ferns for Object Detection,
PAMI(40), No. 2, February 2018, pp. 272-288.
IEEE DOI 1801
Boosting, Feature extraction, Histograms, Object detection, Training, Vegetation, random ferns 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.[Sergio 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

Zhang, S., Benenson, R.[Rodrigo], Omran, M.[Mohamed], Hosang, J.[Jan], Schiele, B.[Bernt],
Towards Reaching Human Performance in Pedestrian Detection,
PAMI(40), No. 4, April 2018, pp. 973-986.
IEEE DOI 1804
Survey, Pedestrian Detection. BibRef
Earlier:
How Far are We from Solving Pedestrian Detection?,
CVPR16(1259-1267)
IEEE DOI 1612
BibRef
Earlier: A2, A3, A4, A5, Only:
Ten Years of Pedestrian Detection, What Have We Learned?,
CVRoads14(613-627).
Springer DOI 1504
convolution, neural nets, object detection, pedestrians, Caltech pedestrian dataset, integral channel features BibRef

Zhang, S., Benenson, R.[Rodrigo], Schiele, B.[Bernt],
CityPersons: A Diverse Dataset for Pedestrian Detection,
CVPR17(4457-4465)
IEEE DOI 1711
Benchmark testing, Detectors, Image segmentation, Semantics, Tools, Training, Urban, areas BibRef

Lu, M.[Meng], Blokpoel, R.[Robbin], Joueiai, M.[Mahtab],
Enhancement of safety and comfort of cyclists at intersections,
IET-ITS(12), No. 6, August 2018, pp. 527-532.
DOI Link 1807
BibRef

Li, D., Zhang, Z., Chen, X., Huang, K.,
A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios,
IP(28), No. 4, April 2019, pp. 1575-1590.
IEEE DOI 1901
feature extraction, image annotation, image recognition, image retrieval, multi-label learning BibRef

Khalifa, A.F.[Ali Farouk], Badr, E.[Eman], Elmahdy, H.N.[Hesham N.],
A survey on human detection surveillance systems for Raspberry Pi,
IVC(85), 2019, pp. 1-13.
Elsevier DOI 1905
Human detection, Machine learning, Raspberry Pi BibRef

Wang, X.[Xiao], Liang, C.[Chao], Chen, C.[Chen], Chen, J.[Jun], Wang, Z.[Zheng], Han, Z.[Zhen], Xiao, C.X.[Chun-Xia],
S3D: Scalable Pedestrian Detection via Score Scale Surface Discrimination,
CirSysVideo(30), No. 10, October 2020, pp. 3332-3344.
IEEE DOI 2010
Feature extraction, Detectors, Proposals, Computational efficiency, Testing, discriminant surface BibRef

Gotovac, S.[Sven], Zelenika, D.[Danijel], Marušic, Ž.[Željko], Božic-Štulic, D.[Dunja],
Visual-Based Person Detection for Search-and-Rescue with UAS: Humans vs. Machine Learning Algorithm,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Chen, L.[Long], Lin, S.B.[Shao-Bo], Lu, X.K.[Xian-Kai], Cao, D.[Dongpu], Wu, H.[Hangbin], Guo, C.[Chi], Liu, C.[Chun], Wang, F.Y.[Fei-Yue],
Deep Neural Network Based Vehicle and Pedestrian Detection for Autonomous Driving: A Survey,
ITS(22), No. 6, June 2021, pp. 3234-3246.
IEEE DOI 2106
Feature extraction, Proposals, Object detection, Computational modeling, Residual neural networks, Detectors, survey BibRef

Niedzielski, T.[Tomasz], Jurecka, M.[Miroslawa], Mizinski, B.[Bartlomiej], Pawul, W.[Wojciech], Motyl, T.[Tomasz],
First Successful Rescue of a Lost Person Using the Human Detection System: A Case Study from Beskid Niski (SE Poland),
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Cao, J.[Jiale], Pang, Y.W.[Yan-Wei], Xie, J.[Jin], Khan, F.S.[Fahad Shahbaz], Shao, L.[Ling],
From Handcrafted to Deep Features for Pedestrian Detection: A Survey,
PAMI(44), No. 9, September 2022, pp. 4913-4934.
IEEE DOI 2208
Feature extraction, Proposals, Cameras, Deep learning, Task analysis, Object detection, Support vector machines, Pedestrian detection, multi-spectral pedestrian detection BibRef

Armando, M.[Matthieu], Boissieux, L.[Laurence], Boyer, E.[Edmond], Franco, J.S.[Jean-Sébastien], Humenberger, M.[Martin], Legras, C.[Christophe], Leroy, V.[Vincent], Marsot, M.[Mathieu], Pansiot, J.[Julien], Pujades, S.[Sergi], Rekik, R.[Rim], Rogez, G.[Grégory], Swamy, A.[Anilkumar], Wuhrer, S.[Stefanie],
4DHumanOutfit: A multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements,
CVIU(237), 2023, pp. 103836.
Elsevier DOI Code:
WWW Link. 2311
Digital human modeling, Multi-view reconstruction, Human motion database BibRef

Ghari, B.[Bahareh], Tourani, A.[Ali], Shahbahrami, A.[Asadollah], Gaydadjiev, G.[Georgi],
Pedestrian detection in low-light conditions: A comprehensive survey,
IVC(148), 2024, pp. 105106.
Elsevier DOI 2407
Survey, Low Light. Survey, Pedestrian Detection. Pedestrian detection, Object detection, Autonomous vehicles BibRef

Hagn, K.[Korbinian], Grau, O.[Oliver],
Validation of Pedestrian Detectors by Classification of Visual Detection Impairing Factors,
SafeDrive22(476-491).
Springer DOI 2304
BibRef

Wiedemer, T.[Thaddäus], Wolf, S.[Stefan], Schumann, A.[Arne], Ma, K.[Kaisheng], Beyerer, J.[Jürgen],
Few-Shot Supervised Prototype Alignment for Pedestrian Detection on Fisheye Images,
L3D-IVU22(4141-4152)
IEEE DOI 2210
Training, Couplings, Adaptation models, Schedules, Surveillance, Prototypes BibRef

Specker, A., Schumann, A., Beyerer, J.,
An Evaluation Of Design Choices For Pedestrian Attribute Recognition In Video,
ICIP20(2331-2335)
IEEE DOI 2011
Computational modeling, Solid modeling, Task analysis, retrieval BibRef

Specker, A.[Andreas], Beyerer, J.[Jürgen],
ReidTrack: Reid-only Multi-target Multi-camera Tracking,
AICity23(5442-5452)
IEEE DOI 2309
BibRef

Specker, A.[Andreas], Florin, L.[Lucas], Cormier, M.[Mickael], Beyerer, J.[Jürgen],
Improving Multi-Target Multi-Camera Tracking by Track Refinement and Completion,
AICity22(3198-3208)
IEEE DOI 2210
Conferences, Urban areas, Topology, Pattern recognition, Task analysis, Artificial intelligence BibRef

Specker, A.[Andreas], Stadler, D.[Daniel], Florin, L.[Lucas], Beyerer, J.[Jürgen],
An Occlusion-aware Multi-target Multi-camera Tracking System,
AICity21(4168-4177)
IEEE DOI 2109
Visualization, Target tracking, Urban areas, Feature extraction, Information filters, Data mining BibRef

Köhl, P., Specker, A., Schumann, A., Beyerer, J.,
The MTA Dataset for Multi Target Multi Camera Pedestrian Tracking by Weighted Distance Aggregation,
VUHCS20(4489-4498)
IEEE DOI 2008
BibRef
Earlier: Dataset, Pedestrians Cameras, Target tracking, Task analysis, Synchronization, Games, Data privacy BibRef

Neumann, L.[Lukáš], Karg, M.[Michelle], Zhang, S.S.[Shan-Shan], Scharfenberger, C.[Christian], Piegert, E.[Eric], Mistr, S.[Sarah], Prokofyeva, O.[Olga], Thiel, R.[Robert], Vedaldi, A.[Andrea], Zisserman, A.[Andrew], Schiele, B.[Bernt],
NightOwls: A Pedestrians at Night Dataset,
ACCV18(I:691-705).
Springer DOI 1906
BibRef

Rasouli, A.[Amir], Kotseruba, I.[Iuliia], Tsotsos, J.K.[John K.],
It's Not All About Size: On the Role of Data Properties in Pedestrian Detection,
CVRoads18(I:210-225).
Springer DOI 1905
BibRef

Eiselein, V., Bochinski, E., Sikora, T.,
Assessing post-detection filters for a generic pedestrian detector in a tracking-by-detection scheme,
AVSS17(1-6)
IEEE DOI 1806
filtering theory, object detection, object tracking, pedestrians, traffic engineering computing, video signal processing, Visualization BibRef

Imaeda, Y.[Yuki], Hirayama, T.[Takatsugu], Kawanishi, Y.[Yasutomo], Deguchi, D.[Daisuke], Ide, I.[Ichiro], Murase, H.[Hiroshi],
Pedestrian Detectability Estimation Considering Visual Adaptation to Drastic Illumination Change,
IEICE(E101-D), No. 5, May 2018, pp. 1457-1461.
WWW Link. 1805
BibRef

Mao, J., Xiao, T., Jiang, Y., Cao, Z.,
What Can Help Pedestrian Detection?,
CVPR17(6034-6043)
IEEE DOI 1711
Convolution, Detectors, Feature extraction, Heating systems, Image edge detection, Image segmentation, Semantics 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).
IEEE DOI 0411
BibRef

Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Counting People, Transportation System Monitoring, Queues .