Detecting Movement Type by Route Segmentation and Classification Karol Waga, Andrei Tabarcea, Minjie...
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Transcript of Detecting Movement Type by Route Segmentation and Classification Karol Waga, Andrei Tabarcea, Minjie...
Detecting Movement Type by Route Segmentation and
Classification
Karol Waga, Andrei Tabarcea,Minjie Chen and Pasi Fränti
MOPSIPROJECTMOPSI
PROJECTUNIVERSITYOF EASTERN
FINLAND
UNIVERSITYOF EASTERN
FINLAND
University of Eastern Finland
JoensuuJoensuuJoki= a riverJoen = of a river
Suu = mouthJoensuu = mouth of a river
Motivation
NokiaAndroidiPhone
None
Trends and popularity of GPS Previous predictions
Nokia: 50% of its smart phones has GPS by 2010-12.
Gartner: 75% has GPS by the end of 2011.
Nokia: 50% of its smart phones has GPS by 2010-12.
Gartner: 75% has GPS by the end of 2011.
Trends and popularity of GPS Current situation
Our lab:Nokia 8 47 %Android 4 24 %iPhone 0 0 %
None 5 30 %
70 %
173 users 7,958 routes
5,208,205 points
Mopsi route collection4th October, 2012
Collected GPS routePlot on map
What is the activity?
Sp
eed
(km
/h)
Time
14
12
10
8
6
4
2
Collected GPS routeTime-vs-speed
0 1000 2000 3000 4000 5000 60000
2
4
6
8
10
12
14
time
spee
destimated segment result
Collected GPS routeGround truth
0 200 400 600 800 10000
5
10
15
20
25
time
spee
destimated segment result
Collected GPS routeAnother example
Summarization of entire route
Existing solutions
Features and classifiers
Sensor data• GPS• GSM, WiFi• Accelerometers• Combination of multiple sensors
Classification• Rule-based vs. trained• Fuzzy logic• Neural networks • Hidden Markov model
Movement type classification
Movement types considered:
Walk Run Bicycle Car
Other possibilities:
Boat Flight
Spatial contextneeded
Skiing
Speed? Track location, season
Train BusTime
tables
Problems attacked
Problems addressed:• Training material is not always available• Problem of over-fit• Loss of generalization
Limitations of current solution:• Correlation between neighboring segments• Accuracy of segmentation
Rule-based!
2-order Hidden Markov model
Proposed solution
Overall algorithm
Optimal segmentation:• Minimize intra-segment speed variance• Detect stop segments
Move type classification:• Speed features• 2-order Hidden Markov Model
Route segmentationDynamic programming
1
1( )j
j j j
i
i i ij
f t t
( , ) min( ( , 1) ( )), (1... 1)
( , ) arg min ( ( , 1) ( ))
sc s c
sc c s c
D s r D c r t t c s
A s r D c r t t
Minimize intra-segment variance:
Optimal segmentation:
O(n2k)
0 1 2 1arg min ( ( , ) ( )), 1...i nm D n i i t t i m
Number of segments
0 10 20 30 40
0.2
0.4
0.6
0.8
1
Speed(km/h)
Pro
bab
ility
BikeRun
WalkStop
Car
Move type classificationA priori probabilities
2 1 1
1 2
( | , ) ( | )
( )
Mi i i i
i i
P m m m P m Xf
P m
i 1 11
( | X , , )M
i i ii
f P m m m
Cost function:
Cost function:
2nd order Hidden Markov Model
Previous segment
Next segment
Probability: Prev.
Next
0.6 - - 0.2 0.2 0.5 0.2 - 0.1 0.2 0.5 - 0.2 0.1 0.2 0.5 - - 0.3 0.2 0.8 - - 0.1 0.1 0.5 0.2 - 0.1 0.2 - 0.6 - 0.2 0.2 - 0.4 0.4 0.1 0.1 - 0.4 - 0.4 0.2 - 0.8 - 0.1 0.1
Probability: Prev.
Next
0.5 - 0.2 0.1 0.2 - 0.4 0.4 0.1 0.1 - - 0.4 0.4 0.2 - - 0.4 0.4 0.2 - - 0.8 0.1 0.1 0.5 - - 0.3 0.2 - 0.4 - 0.4 0.2 - - 0.4 0.4 0.2 0.2 0.2 0.2 0.2 0.2 - - 0.1 0.7 0.2 0.8 - - 0.1 0.1 - 0.8 - 0.1 0.1 - - 0.8 0.1 0.1 - - 0.1 0.7 0.2 0.2 0.2 0.2 0.2 0.2
Rule-based model (HMM)
Experiments
Segmentation of car route
Separating stop segments
Long distance running
Overall statisticsfrom running by move type
Interval training
Intervals
Warm-up &slow-down
Stops
Bicycle trip represented as carAlgorithm tries to be too clever
What next?
Further improvements
Boat Flight SkiingTrain Bus
More move types
Better stop detection
Generate ground truth
New movement types
Train
Skiing Flight
Conclusions
Method that (usually) works!
Simple to implement
No training data required