![](https://csdnimg.cn/release/download_crawler_static/12913511/bg7.jpg)
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Dataset Year Description #Cites
MIT Ped.[30] 2000 One of the first pedestrian detection datasets. Consists of ∼500 training and ∼200
testing images (built based on the LabelMe database). url: http://cbcl.mit.edu/
software-datasets/PedestrianData.html
1515
INRIA [12] 2005 One of the most famous and important pedestrian detection datasets at early time.
Introduced by the HOG paper [12]. url: http://pascal.inrialpes.fr/data/human/
24705
Caltech
[59, 60]
2009 One of the most famous pedestrian detection datasets and benchmarks. Consists
of ∼190,000 pedestrians in training set and ∼160,000 in testing set. The metric
is Pascal-VOC @ 0.5 IoU. url: http://www.vision.caltech.edu/Image Datasets/
CaltechPedestrians/
2026
KITTI [61] 2012 One of the most famous datasets for traffic scene analysis. Captured in Karl-
sruhe, Germany. Consists of ∼100,000 pedestrians (∼6,000 individuals). url:
http://www.cvlibs.net/datasets/kitti/index.php
2620
CityPersons
[62]
2017 Built based on CityScapes dataset [63]. Consists of ∼19,000 pedestrians in training
set and ∼11,000 in testing set. Same metric with CalTech. url: https://bitbucket.
org/shanshanzhang/citypersons
50
EuroCity [64] 2018 The largest pedestrian detection dataset so far. Captured from 31 cities in 12
European countries. Consists of ∼238,000 instances in ∼47,000 images. Same
metric with CalTech.
1
TABLE 2
An overview of some popular pedestrian detection datasets.
Dataset Year Description #Cites
FDDB [65] 2010 Consists of ∼2,800 images and ∼5,000 faces from Yahoo! With occlusions, pose
changes, out-of-focus, etc. url: http://vis-www.cs.umass.edu/fddb/index.html
531
AFLW [66] 2011 Consists of ∼26,000 faces and 22,000 images from Flickr with rich facial landmark
annotations. url: https://www.tugraz.at/institute/icg/research/team-bischof/
lrs/downloads/aflw/
414
IJB [67] 2015 IJB-A/B/C consists of over 50,000 images and videos frames, for both
recognition and detection tasks. url: https://www.nist.gov/programs-projects/
face-challenges
279
WiderFace
[68]
2016 One of the largest face detection dataset. Consists of ∼32,000 images and 394,000
faces with rich annotations i.e., scale, occlusion, pose, etc. url: http://mmlab.ie.
cuhk.edu.hk/projects/WIDERFace/
193
UFDD [69] 2018 Consists of ∼6,000 images and ∼11,000 faces. Variations include weather-based
degradation, motion blur, focus blur, etc. url: http://www.ufdd.info/
1
WildestFaces
[70]
2018 With ∼68,000 video frames and ∼2,200 shots of 64 fighting celebrities in uncon-
strained scenarios. The dataset hasn’t been released yet.
2
TABLE 3
An overview of some popular face detection datasets.
robot arm trying to grasp a spanner).
Recently, there are some further developments of the
evaluation in the Open Images dataset, e.g., by considering
the group-of boxes and the non-exhaustive image-level cate-
gory hierarchies. Some researchers also have proposed some
alternative metrics, e.g., “localization recall precision” [94].
Despite the recent changes, the VOC/COCO-based mAP is
still the most frequently used evaluation metric for object
detection.
2.3 Technical Evolution in Object Detection
In this section, we will introduce some important building
blocks of a detection system and their technical evolutions
in the past 20 years.
2.3.1 Early Time’s Dark Knowledge
The early time’s object detection (before 2000) did not follow
a unified detection philosophy like sliding window detec-
tion. Detectors at that time were usually designed based on
low-level and mid-level vision as follows.
• Components, shapes and edges
“Recognition-by-components”, as an important cogni-
tive theory [98], has long been the core idea of image
recognition and object detection [13, 99, 100]. Some early
researchers framed the object detection as a measurement of
similarity between the object components, shapes and con-
tours, including Distance Transforms [101], Shape Contexts
[35], and Edgelet [102], etc. Despite promising initial results,
things did not work out well on more complicated detec-