视频处理_The PASCAL Visual Object Classes Challenge 2007(PASCAL视觉目标分类挑战赛2007)

The PASCAL Visual Object Classes Challenge

2007(PASCAL视觉目标分类挑战赛2007)

数据摘要：

The goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are:

Person: person

Animal: bird, cat, cow, dog, horse, sheep

Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train

Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor

There will be two main competitions, and two smaller scale ""taster"" competitions.

The training data provided consists of a set of images; each image has an annotation file giving a bounding box and object class label for each object in one of the twenty classes present in the image. Note that multiple objects from multiple classes may be present in the same image.

This is a direct replacement for that provided for the challenge but additionally includes full annotation of each test image, and segmentation ground truth for the segmentation taster images. The annotated test data additionally contains information about the owner of each image as provided by flickr.

中文关键词：

PASCAL,视觉目标分类,挑战赛,目标识别,现实场景,

英文关键词：

PASCAL,Visual Object Classes,challenge,objects recognition,realistic scenes,

数据格式：

VIDEO

数据用途：

The goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided.

数据详细介绍：

The PASCAL Visual Object Classes Challenge 2007

Introduction

The goal of this challenge is to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). It is fundamentally a supervised learning learning problem in that a training set of labelled images is provided. The twenty object classes that have been selected are:

∙Person: person

∙Animal: bird, cat, cow, dog, horse, sheep

∙Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train

∙Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor There will be two main competitions, and two smaller scale "taster" competitions.

Main Competitions

1. Classification: For each of the twenty classes, predicting

presence/absence of an example of that class in the test image.

2. Detection: Predicting the bounding box and label of each object

from the twenty target classes in the test image.

20 classes

Participants may enter either (or both) of these competitions, and can choose to tackle any (or all) of the twenty object classes. The challenge allows for two approaches to each of the competitions: