Penn-Fudan Database for Pedestrian Detection and Segmentation
2D Box
Autonomous Driving
License: Unknown


This is an image database containing images that are used for pedestrian detection. The images are taken from scenes around campus and urban street. The objects we are interested in these images are pedestrians. Each image will have at least one pedestrian in it.

Data Annotation

The annotation format is compatible with PASCAL Annotation Version 1.00.

Data Format

The heights of labeled pedestrians in this database fall into [180,390] pixels. All labeled pedestrians are straight up.
There are 170 images with 345 labeled pedestrians, among which 96 images are taken from around University of Pennsylvania, and other 74 are taken from around Fudan University.


author="Wang, Liming
and Shi, Jianbo
and Song, Gang
and Shen, I-fan",
editor="Yagi, Yasushi
and Kang, Sing Bing
and Kweon, In So
and Zha, Hongbin",
title="Object Detection Combining Recognition and Segmentation",
booktitle="Computer Vision -- ACCV 2007",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
abstract="We develop an object detection method combining top-down recognition with bottom-up image segmentation. There are two main steps in this method: a hypothesis generation step and a verification step. In the top-down hypothesis generation step, we design an improved Shape Context feature, which is more robust to object deformation and background clutter. The improved Shape Context is used to generate a set of hypotheses of object locations and figure-ground masks, which have high recall and low precision rate. In the verification step, we first compute a set of feasible segmentations that are consistent with top-down object hypotheses, then we propose a False Positive Pruning(FPP) procedure to prune out false positives. We exploit the fact that false positive regions typically do not align with any feasible image segmentation. Experiments show that this simple framework is capable of achieving both high recall and high precision with only a few positive training examples and that this method can be generalized to many object classes.",
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