图像处理_Daimler Pedestrian Classification Benchmark Dataset(戴姆勒行人分类标准数据库)

Daimler Pedestrian Classification Benchmark Dataset(戴姆勒行人分类标准数据库)
数据摘要：
The dataset contains a collection of pedestrian and non-pedestrian images. It is made available for download on this site for benchmarking purposes, in order to advance research on pedestrian classification.
The dataset consists of two parts:
a base data set. The base data set contains a total of 4000 pedestrian- and 5000 non-pedestrian samples cut out from video images and scaled to common size of 18x36 pixels. This data set has been used in Section
VII-A of the paper referenced above.
Pedestrian images were obtained from manually labeling and extracting the rectangular positions of pedestrians in video images. Video images were recorded at various (day) times and locations with no particular constraints on pedestrian pose or clothing, except that pedestrians are standing in upright position and are fully visible. As non-pedestrian images, patterns representative for typical preprocessing steps within a pedestrian classification application, from video images known not to
contain any pedestrians. We chose to use a shape-based pedestrian detector that matches a given set of pedestrian shape templates to distance transformed edge images (i.e. comparatively relaxed matching threshold).
additional non-pedestrian images. An additional collection of 1200 video images NOT containing any pedestrians, intended for the extraction of additional negative training examples. Section V of the paper referenced above describes two methods on how to increase the training sample size from these images, and Section VII-B lists experimental results.
中文关键词：
分类,标准,位置,行人,视频,
英文关键词：
Classification,Benchmark,positions,pedestrians,video,
数据格式：
IMAGE
数据用途：
In order to advance research on pedestrian classification
数据详细介绍：
Daimler Pedestrian Classification Benchmark Dataset
The dataset contains a collection of pedestrian and non-pedestrian images. It is made available for download on this site for benchmarking purposes, in order to advance research on pedestrian classification.
The dataset consists of two parts:
∙ a base data set. The base data set contains a total of 4000 pedestrian- and 5000 non-pedestrian samples cut out from video images and
scaled to common size of 18x36 pixels. This data set has been used in Section VII-A of the paper referenced above.
Pedestrian images were obtained from manually labeling and extracting the rectangular positions of pedestrians in video images. Video images were recorded at various (day) times and locations with no particular
constraints on pedestrian pose or clothing, except that pedestrians are
standing in upright position and are fully visible. As non-pedestrian
images, patterns representative for typical preprocessing steps within a pedestrian classification application, from video images known not to
contain any pedestrians. We chose to use a shape-based pedestrian
detector that matches a given set of pedestrian shape templates to
distance transformed edge images (i.e. comparatively relaxed matching threshold).
∙additional non-pedestrian images. An additional collection of 1200 video images NOT containing any pedestrians, intended for the
extraction of additional negative training examples. Section V of the
paper referenced above describes two methods on how to increase the training sample size from these images, and Section VII-B lists
experimental results.
License Terms
This dataset is made available to the scientific community for non-commercial research purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use, copy, and distribute the data given that you agree:
1. That the dataset comes "AS IS", without express or implied warranty.
Although every effort has been made to ensure accuracy,
DaimlerChrysler (or the University of Amsterdam, as website host) does not accept any responsibility for errors or omissions.
2. That you include a reference to the above publication in any published
work that makes use of the dataset.
3. That if you have altered the content of the dataset or created derivative
work, prominent notices are made so that any recipients know that they are not receiving the original data.
4. That you may not use or distribute the dataset or any derivative work for
commercial purposes as, for example, licensing or selling the data, or
using the data with a purpose to procure a commercial gain.
5. That this original license notice is retained with all copies or derivatives
of the dataset.
6. That all rights not expressly granted to you are reserved by
DaimlerChrysler.
数据预览：
点此下载完整数据集。