abandoned object detection for indoor public surveillance video dept. of computer science national...
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Abandoned Object Detection for Indoor Public Surveillance Video
Dept. of Computer ScienceNational Tsing Hua University
Outline Introduction
Common Surveillance Scenarios and Schemes Scenario of Few Pedestrians Scenario of Normal Case Scenario of Rush Hours
Proposed Abandoned Object Detection Scheme
Experimental Results
Conclusions and Future Works
Applications of Video Surveillance Systems
Security Surveillance of housing, public area Detecting or tracking suspicious objects
Behavior analysis Segmentation of the human body Classify the behavior of the human
Scenario Types
Few Pedestrians (Lib)
Normal Case (DingXi Station)
Rush Hours (Taipei Main Station)
Object Presence Frequently
Object Presence Occasionally
Object Presence Rarely
Scenarios of Environments
Scenarios Types
Object Presence Object Detection
Few Few PedestriansPedestrians FrequentFrequent Background Background
SubtractionSubtraction
Normal CaseSomewhat Frequently
Simple Motion Filter
Rush Hours RareAdvanced
Motion Filter
Scenarios of Environments
Scenarios Types
Object Presence Object Detection
Few Pedestrians
FrequentBackground Subtraction
Normal CaseNormal Case Somewhat Somewhat FrequentlyFrequently
Simple Motion Simple Motion FilterFilter
Rush Hours RareAdvanced
Motion Filter
Scenario of Normal Case- Most Frequent Intensity
X
Frame Counter
Pixel Intensity
0255Background or
Stationary Objects
Most Frequent Intensity !!
Scenario of Normal Case- Most Frequent Intensity
The reference background The Most Frequent IntensityMost Frequent Intensity Picture
Scenarios of Environments
Scenarios Types
Object Presence Object Detection
Few Pedestrians
FrequentBackground Subtraction
Normal CaseSomewhat Frequently
Simple Motion Filter
Rush HoursRush Hours RareRareAdvanced Advanced
Motion FilterMotion Filter
Proposed Abandoned Object Detection Scheme for Scenario of Rush Hours Pixel-based
MoG
Advanced Motion Filter for Scenario of Rush Hours Using Vertical Scan Line Eliminate the Sparse Background Clutter Extracting the Complete Shape of an Abandoned Object Tracing Through Vertical Scan Lines Controllable System Alarm Response Time Grouping Abandoned Pixels to Objects
A Multi-model Background Modeling Algorithm - Mixture of Gaussian (MoG)
1
frame #
weight
0
x
Background distribution
Proposed Motion Filter -Eliminate the Sparse Background Clutter
The referenced background Current frame
Proposed Motion Filter-Extracting the Complete Shape of the Abandoned Object
The referenced background Current frame
Proposed Motion Filter -Extracting the Complete Shape of the Abandoned Object
First foreground point Complete Shape
Proposed Motion Filter -Tracing Through Vertical Scan Lines
x
Stop at first foreground section
Tracing through the next foreground sectionCurrent frame
Proposed Motion Filter-Controllable System Alarm Response Time
Different reasonable response time for different applications
Avoid to issue the alarm for temporally still pedestrians
Proposed Motion Filter -Grouping Abandoned Pixels to Objects
Background Pixel
Abandoned Object Pixel
Constraint: Object size ≥ 4 pixels
One Alarm
Experimental Results-Test Sequences and
Parameters
Sequence Name Total Frames
The Amount of Pedestrians
Abandoned Object is Shot First
Taipei Station Metro
1200 Rush Hours In the 99th Frame
DingXi Metro 1000 Normal Case In the 1st Frame
NTHU Library 1000 Few In the 1st Frame
Experimental Results-Evaluating Parameters
Application-depended Thresholds Eliminate the Sparse Background Clutter
Te Size of an Abandoned Object
Ts Controllable System Alarm Responding Time
Tr Performance Evaluation
Response Time (<25s) Alarms for Abandoned Objects / Total Alarms
↑
Eliminate the Sparse Background Clutter (Taipei Station)
475
685
861
0 200 400 600 800 1000
Te=1
Te=10
Te=20
Number of Frames
Response Time
2
14 14
5
3
7
4
1
3
0
2
4
6
8
10
12
14
16
Abandoned Objects Still Pedestrians False Alarms
Num
ber o
f Ala
rms Te=1
Te=10
Te=20
Alarms Count
<25s 2/(2+14+14)=1/15 5/(5+3+7)=1/3
Size of an Abandoned Object (Taipei Station)
641
685
845
0 100 200 300 400 500 600 700 800 900
Ts=50
Ts=100
Ts=200
Number of Frames
Response Time
78
15
5
3
7
3
0
2
0
2
4
6
8
10
12
14
16
Abandoned Objects Still Pedestrians False AlarmsN
umbe
r of A
larm
s
Ts=50
Ts=100
Ts=200
Alarms Count
7/(7+8+15)=7/30 5/(5+3+7)=1/3<25s
Controllable System Alarm Responding Time (Lib)
78
328
578
0 100 200 300 400 500 600 700
Tr=1
Tr=250
Tr=500
Number of Frames
Response Time
14
4
0
13
0 0
13
0 00
2
4
6
8
10
12
14
16
Abandoned Objects Still Pedestrians False AlarmsN
umbe
r of A
larm
s
Tr=1
Tr=250
Tr=500
Alarms Count
<25s
Experimental Results-Comparisons with Related Works
1
31
76
2
14
118
5 3 7
0
20
40
60
80
100
120
140
Abandoned Objects Still Pedestrians False Alarms
Num
ber o
f Alar
ms
Change DetectQ[11]
Multi- Background[12]
Proposed
[11]
[12]Demo
Experimental Results-Time Complexity Analysis
Process Time
0
40
80
120
160
Complete
Area
Reduce
Area
Skip Pixels Reudce
Area +Skip
Pixels
Mill
iSec
ond
Process Time47.7
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