User:Ke CHEN/Proposed/Gait recognition

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With the increasing demands of visual surveillance systems, human identification at a distance is an urgent need. Gait is an attractive biometric feature for human identification at a distance, and recently has gained much interest from computer vision researchers. Gait is a particular way or manner of moving on foot. Compared with those traditional biometric features, such as face, iris, palm print and finger print, gait has many unique advantages such as non-contact, non-invasive and perceivable at a distance.


A Framework for Gait Recognition

Figure 1: A framework for gait recognition systems

A typical gait recognition system is shown in Figure 1. Video data is firstly captured by a camera, and then walking persons are detected and segmented from the background by some motion detection and segmentation methods. After silhouettes are segmented from the background, gait features can be extracted. Human identification can be achieved by measuring the similarity between the extracted gait feature and those in a gait database.

Normally gait features are not robust enough to view variant, clothing changing, carrying bags, etc. To extract robust and discriminative feature is an important step in gait recognition. Here we will focus on gait feature extraction.

Gait Features

Databases and Evaluation

Even though many gait recognition algorithms have been proposed, comparison of different algorithms and evaluation of an algorithm's robustness to some variations such as the variations of view angle, clothing, shoe types, surface types, carrying condition, illumination, and time are still hard and open problems. These variations should be fully studied to develop robust and accurate gait recognition algorithms.

The HumanID Gait Challenge Problem

The HumanID Gait Challenge Problem \cite{usf:db:pami}, which consists of a large database, a baseline algorithm and twelve experiments, tried to handle these problems. The data in the HumanID Gait Challenge Problem was collected in an outdoor environment with complex background, The twelve experiments were designed to evaluate an algorithm's robustness to view, shoe, surface, time, clothing and carrying condition changes.

CASIA Gait Database and Evaluation Metrics

In the CASIA Gait Database there are three datasets: Dataset A, Dataset B (multi-view dataset) and Dataset C (infrared dataset).

Dataset A (former NLPR Gait Database) was created on Dec. 10, 2001, including 20 persons. Each person has 12 image sequences, 4 sequences for each of the three directions, i.e. parallel, 45 degrees and 90 degrees to the image plane. The length of each sequence is not identical for the variation of the walker's speed, but it must ranges from 37 to 127. The size of Dataset A is about 2.2GB and the database includes 19139 images.

Dataset B is a large multi-view gait database, which is created in January 2005. There are 124 subjects, and the gait data was captured from 11 views. Three variations, namely view angle, clothing and carrying condition changes, are separately considered. Besides the video files, we still provide human silhouettes extracted from video files.

Dataset C was collected by an infrared (thermal) camera in Jul.-Aug. 2005. It contains 153 subjects and takes into account four walking conditions: normal walking, slow walking, fast walking, and normal walking with a bag. The videos were all captured at night.

Other Databases

Databases used in recent work
Database Name Num. of Subjects Num. of Sequences Environment Time Variations
UCSD Database cite{ucsd:database} 6 42 Outdoor 1998 -
MIT AI Database cite{lee:ellipsoidal} 24 194 Indoor 2001 View, time
Georgia Tech Database cite{gatech:web} 20 188 Outdoor, indoor, magnetic tracker 2001 View, time, distance
CMU Mobo Database cite{cmu:web,cmu:db} 25 600 Indoor, treadmill Mar. 2001 6 viewpoints, speed, carrying condition, incline surface
HID-UMD Database(Dataset 1) 25 100 Outdoor Feb.-May 2001 4 viewpoints
HID-UMD Database(Dataset 2) 55 220 Outdoor June-July 2001 2 viewpoints
Soton Small Database cite{soton:web} 12 - Indoor, green chroma-key backdrop Carrying condition, clothing, shoe, view -
Soton Large Database cite{soton:db,soton:web} 115 2,128 Indoor, outdoor, treadmill Summer, 2001 View
Gait Challenge Database cite{usf:db:pami,usf:web} 122 1,870 Outdoor May and Nov. 2001 2 viewpoints, surface, shoe, carrying condition, time
CASIA Database(Dataset A) cite{cbsr:web} 20 240 Outdoor Dec. 2001 3 viewpoints
CASIA Database(Dataset B) cite{yu:casiadb,cbsr:web} 124 13,640 Indoor Jan. 2005 11 viewpoints, clothing, carrying condition
CASIA Database(Dataset C) cite{cbsr:web} 153 1,530 Outdoor, at night, thermal camera Jul.-Aug. 2005 Speed, carrying condition
TUM-IITKGP Database 35 840 Indoor 2010 hand-in-pocket, backpack, gown, static occlusion, dynamic occlusion
TUM-GAID Database 305 3,370 Indoor, Kinect, Audio + Image + Depth Jan. and Apr. 2012 backpack, coating shoes, time

Problems and Challenges

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