FIMD: Fine-grained

Device-free Motion Detection

Jiang Xiao, Kaishun Wu, Youwen Yi, Lu Wang, Lionel M. Ni

Department of Computer Science and Engineering

Hong Kong University of Science and Technology

Dec. 18th, 2012

ICPADS 2012

Outline

• Introduction & Motivation

• Related Work

• System Design

• Performance Evaluation

• Conclusion

2

Device-free Motion Detection is in need!

I won’t be caught

3

Security for intrusion

Introduction

Current Detection Methods Camera Infrared

Environment

constraints

(e.g., dark, smoke)

Line-of-sight

Ultrasonic

High cost for

large environment

Sensors

High cost for

dense deployment

Zhang et. al

[PerCom’07, ’09, ’11]

4

Can’t work well in large scale, complicate,

typical indoor environment!

Motivation

WLAN Advantages

Low Cost

Easy Implementation

5

Outline

• Introduction & Motivation

• Related Work

• System Design

• Performance Evaluation

• Conclusion

6

Related Work

Static

Motion Happens

Idea: RSS becomes anomalous when the environment changes

Received Signal Strength Indicator (RSSI)

Youssef et. al. [MobiCom’07][PerCom’09][PerCom’12]

7

RF Signal

Transmitter Receiver

Limitations of RSS-based Techniques 1. Narrowband interference

high false alarm rate

Hard to distinguish narrowband interference from motion dynamic

8

2. RSS is of high variability

miss detection Node ID

Slow dynamic is easily hidden by the inherent RSS variance

A reliable method is in need.

resist from the narrowband interference

in the 2.4GHz band

temporal stable in static while

sensitive to a motion instantly

9

Challenge 1

Could we find such a reliable method?

10

2.4GHz

CSI Property 1 –

Frequency diversity

Key Insight

single value

RSSI Receiver CSIs

S/P FFT

Baseband

multiple values

RF band

11 CSI-based Indoor Localization: FILA[INFOCOM’12]

Data out OFDM

Transmitter

Channel

Data in OFDM

Receiver × +

Key Insight

In OFDM system, the received signal over multiple subcarriers is

amplitude phase

Channel gain CSI

12

RSS: variant CSI: relatively stable

Key Insight

CSI Property 2 –

Temporal Stability 13

CSI

am

plit

ud

e

RSS

I (d

Bm

)

Time Duration (s) Time Duration (s)

We want to harness fine-grained CSI

for device-free indoor motion detection.

14

Goal

To improve detection rate

To reduce false alarm rate

To improve detection accuracy

with low cost

15

Our Contributions

Exploit the possibility of CSI

Extract suitable CSI features

Propose burst detection alg.

16

Challenges

1.

Could we find a reliable method

for device-free motion detection?

2.

How to resist from

narrowband interference?

3.

How to distinguish motion event

from noise?

Outline

• Introduction & Motivation

• Related Work

• System Design

• Performance Evaluation

• Conclusion

17

Static Map Update AP

RF S

ignal

FIMD Design

CSI Feature Extraction

Burst Detection

Static Map Construction

False alarm

Filter

Static Map Update

DP CSI

Collection

Release Alarm

Server

18

Outline

• Introduction & Motivation

• Related Work

• System Design

– CSI Feature Extraction

– Burst Detection

– False Alarm Filter

• Performance Evaluation

• Conclusion

19

Challenge 2

How to extract the features of CSI that

resist from narrowband interference?

20

1. CSI Feature Extraction

Length n+1 Start

Process CSIs

W

Target: to extract CSI features can reflect static/dynamic patterns

21

H1,1, H2,1, …, Hk,1, Hk+1,1, … Hk+n,1, …

H1,2, H2,2, …, Hk2, Hk+1,2, … Hk+n,2, …

H1,30, H2,30, …, Hk,30 Hk+1,30, … Hk+n,30, …

…

…

…

…

…

subca

rrie

rs

[Hk, Hk+1, … Hk+n]

raw CSIs

1. CSI Feature Extraction

Process CSIs

Feature value vector V V= (eigen (C)/n+1)

Target: to extract CSI features can reflect static/dynamic patterns

22

Compute correlation factor

btw. each column of

[Hk, Hk+1, … Hk+n]

1st max & 2nd max eigenvalues are chosen!

1. CSI Feature Extraction

Target: to extract CSI features can reflect static/dynamic patterns

23

[Hk, Hk+1, … Hk+n]

V= (eigen (C)/n+1)

1. CSI Feature Extraction

CSI feature can reveal normal/motion behavior

Target: to extract CSI features can reflect static/dynamic patterns

24

1st max eigenvalue

2nd m

ax e

igen

val

ue

RSS: more variant

CSI: relatively stable

Robust to narrowband interference

1. CSI Feature Extraction

In presence of narrowband interference, e.g., Bluetooth

25

Outline

• Introduction & Motivation

• Related Work

• System Design

– CSI Feature Extraction

– Burst Detection

– False Alarm Filter

• Performance Evaluation

• Conclusion

26

“burst”

27

0 20 40 60 80 100 120 1400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

static dynamic static

2n

d

eige

nva

lue

1

st

eige

nva

lue

28

0.4 0.5 0.6 0.7 0.8 0.9 10

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Feature X

Featu

re Y

Ground Truth

1st max eigenvalue

2nd m

ax e

igen

val

ue

Ground Truth

Challenge 3

How to distinguish motion event

from noise?

29

2. Burst Detection

Method: apply DBSCAN alg. [Ester, et al. (KDD’96)]

Fact: patterns of motion are diff. from static ones

No prior knowledge of the # of clusters

Discovery arbitrary shape clusters

Classify CSI

Patterns

Target: to monitor the “burst” motion occurrence

30

2. Burst Detection

Method: DBSCAN clustering

ε – max. radius of the neighborhood

Nε (Vi): {Vj belongs to Nε | Dist(Vi,Vj) ≤ ε}

minPts – min. # of points in an ε-neighbor to form a cluster

for each point in a cluster, ε-neighbouor has to contain

≥ minPts.

Density = the # of points within a specified radius ε

Target: to monitor the “burst” motion occurrence

31

2. Burst Detection

Euclidean distance for DBSCAN clustering

Target: to monitor the “burst” motion occurrence

32

0.4 0.5 0.6 0.7 0.8 0.9 10

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

Feature X

Featu

re Y

DBSCAN Result

2nd m

ax e

igen

val

ue

1st max eigenvalue

Outline

• Introduction & Motivation

• Related Work

• System Design

– CSI Feature Extraction

– Burst Detection

– False Alarm Filter

• Performance Evaluation

• Conclusion

33

3. False Alarm Filter

Target: to reduce the false alarm rate

Burst

false alarm!

Burst Burst

34

true motion!

Fact: a single motion instance always lasts a short period

Outline

• Introduction & Motivation

• Related Work

• System Design

• Performance Evaluation

• Conclusion

35

Hardware

Commercial NICs, APs

Software

Linux 2.6.38 kernel, Matlab, Python

Testbeds

2 typical indoor

environments in HKUST

Size

–7m × 11m (Lab)

–32.5m×1.5m (Corridor)

Experimental Setup

802.11n Router Intel WiFi Link 5300

Corridor Lab

36

Evaluation Metrics

True Positive

(TP) Rate

False Positive (FP) Rate

False Negative

(FN) Rate

Condition: Motion Condition: Static Te

st o

utco

me:

Motion

Test

out

com

e:

Sta

tic Ignore

37

Effectiveness of FIMD

Detection performance w.r.t false alarm is high!

1%

DR > 70%

9%

DR>90%

Detection performance w.r.t false alarm (ROC Curve)

38

Effectiveness of FIMD

The longer the window length, the less sensitivity

Influence of the length of sliding window

39

Effectiveness of FIMD

The longer the window length, the lower FP rate

Influence of the length of sliding window

40

CSI vs. RSS

Without Narrowband Interference

41

Lab Corridor0

0.2

0.4

0.6

0.8

1D

ete

cti

on

Rate

CSI-based

RSSI-based

Refer to [PerCom’12] Youssef et. al. RASID system

• kernel density-based approach

CSI vs. RSS

No Motion, with Narrowband Interference

Bluetooth

42

SW=5 SW=8 SW=11 SW=14 SW=17 SW=200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Fals

e P

osi

tiv

e

CSI-based

RSS-based

Outline

• Introduction & Motivation

• Related Work

• System Design

• Performance Evaluation

• Conclusion

43

We presented a novel device-free indoor motion detection

system with commodity hardware in large indoor scenarios.

We leverage both the frequency diversity and temporal

stability of CSI to enhance detection performance.

Experimental results show that CSI-based approach is

superior to RSS-based approach in RF domain.

Conclusion

44

Thanks.

Questions?

jxiao@cse.ust.hk

PhD Candidate @ Hong Kong University of Sci.& Tech.