people detection algorithms based on appearance and motion information

88 Pages·2009·2.86 MB·English

UNIVERSIDAD AUT(cid:211)NOMA DE MADRID
ESCUELA POLIT(cid:201)CNICA SUPERIOR
people detection algorithms based
on appearance and motion
information
Alvaro Garc(cid:237)a Mart(cid:237)n
Supervisor: JosØ Mar(cid:237)a Mart(cid:237)nez SÆnchez
-TRABAJO FIN DE MASTER-
Departamento de Ingenier(cid:237)a InformÆtica
Escuela PolitØcnica Superior
Universidad Aut(cid:243)noma de Madrid
November 2009

people detection algorithms based
on appearance and motion
information
Alvaro Garc(cid:237)a Mart(cid:237)n
Supervisor: JosØ Mar(cid:237)a Mart(cid:237)nez SÆnchez
email: {Alvaro.Garcia, JoseM.Martinez}@uam.es
Video Processing and Understanding Lab
Departamento de Ingenier(cid:237)a InformÆtica
Escuela PolitØcnica Superior
Universidad Aut(cid:243)noma de Madrid
November 2009

Abstract
.The work presented in this document is focused on the improvement of the people
detection task in visual analysis in video surveillance sequences. We have designed
and implemented a people detection algorithm based on appearance and motion as
discriminative information. Firstly, we have undertaken a study on the state of the
art in people detection algorithms targeted to video surveillance scenarios and we
have made a classi(cid:28)cation of the di(cid:27)erent algorithms studied. Within this classi(cid:28)ca-
tion, algorithms based on appearance use to obtain better results than motion based
algorithms, and therefore we have designed and implement an algorithm based on ap-
pearance but adding robustness to the detection with motion information. As main
result a new people detector have been implemented and integrated into the VPU-
Lab system: this new algorithm obtains more reliable results and less computational
cost than the previous algorithm implemented by VPULab. Additionally, in order to
provide a good performance evaluation of the proposed framework and a comparison
with previous work: the design and annotation of a people video/image dataset have
been done.
v
This work is supported by CÆtedra Infoglobal-UAM para (cid:16)Nuevas tecnolog(cid:237)as de
v(cid:237)deo aplicadas a la seguridad(cid:17), the Spanish Government (TEC2007- 65400 Seman-
ticVideo) and the Comunidad de Madrid (S-050/TIC-0223 - ProMultiDis-CM)
Acknowledgements
First I would like to thank my supervisor, Prof. Dr. JosØ Mar(cid:237)a Mart(cid:237)nez SÆnchez,
for their support and advice during this work.
I also wish to thank all members of Video Processing & Understanding Lab for
their support and friendship.
Finally, I would like to thank my family and friends for always being there sup-
porting me.
`lvaro Garc(cid:237)a Mart(cid:237)n
November 2009
vii

Contents
Abstract v
Acknowledgements vii
1 Introduction 1
1.1 Motivation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Document Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 State Of The Art 4
2.1 People Detection System . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Classi(cid:28)cation of People Detection Algorithms . . . . . . . . . . . . . . 6
2.2.1 Methods Based on Motion . . . . . . . . . . . . . . . . . . . . . 7
2.2.2 Methods Based on Appearance . . . . . . . . . . . . . . . . . . 8
2.2.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3 People Detector 16
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2 Previous Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3 Proposed People Detector . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.3.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.3.2 Image Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3.3 Proposed Algorithm Design . . . . . . . . . . . . . . . . . . . . 20
3.3.4 Proposed Final System Design . . . . . . . . . . . . . . . . . . 28
3.4 Motion Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4.1 Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4.2 Improving People Detection by Using Motion Information . . . 31
ix
CONTENTS x
4 Integration 33
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2 External Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.3 Internal Structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.3.1 Edge People Detector . . . . . . . . . . . . . . . . . . . . . . . 34
4.3.2 Fusion Edge People Detector . . . . . . . . . . . . . . . . . . . 35
5 Experimental Work 37
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.2 Experimental data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.2.1 Dataset Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.2.2 Ground Truth . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.3 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.4 Performance evaluation comparison . . . . . . . . . . . . . . . . . . . . 43
5.4.1 People Detection Results. . . . . . . . . . . . . . . . . . . . . . 43
5.4.2 Computational Cost . . . . . . . . . . . . . . . . . . . . . . . . 54
6 Conclusions and future work 56
6.1 Summary of Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
6.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
6.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Bibliography 59
List of Abbreaviations 62
Appendix 62
A GML AdaBoost Matlab Toolbox 63
A.1 Functions and Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
A.1.1 GML Functions and Classes . . . . . . . . . . . . . . . . . . . . 63
A.1.2 Functions and Classes Implemented . . . . . . . . . . . . . . . 65
B C++ classes 67
B.1 VPULab Classes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
B.2 Classes Implemented . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
C ViPER Ground Truth Tool 71
C.1 ViPER-GT Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
C.2 Creating Schema Descriptions . . . . . . . . . . . . . . . . . . . . . . . 72