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VLDB 2008. TRACLASS : Trajectory Classification Using Hierarchical Region-Based and Trajectory-Based Clustering Jae-Gil Lee, Jiawei Han, Xiaolei Li, Hector Gonzalez University of Illinois at Urbana-Champaign. Outline. Motivation TraClass : Trajectory Feature Generation - PowerPoint PPT Presentation
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TRACLASS: TRAJECTORY CLASSIFI-CATION USING HIERARCHICAL RE-GION-BASED AND TRAJECTORY-BASED CLUSTERING
JAE-GIL LEE, JIAWEI HAN, XIAOLEI LI, HECTOR GONZALEZUNIVERSITY OF ILLINOIS AT URBANA-CHAM-PAIGN
VLDB 2008
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Outline Motivation TraClass: Trajectory Feature Generation
Trajectory Partitioning Region-Based Clustering Trajectory-Based Clustering
Classification Strategy Performance Evaluation Related Work Conclusions
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Classification
Feature Gener-ation
NAME RANK YEARS TENUREDMike Assistant Prof 3 noMary Assistant Prof 7 yesBill Professor 2 yesJim Associate Prof 7 yesDave Assistant Prof 6 noAnne Associate Prof 3 no
Classi-fier
Class la-bel
Training data
Fea-tures
Prediction
Unseen data(Jeff, Professor, 4, ?)
Tenured = Yes
Scope of this paper
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Trajectory Data A trajectory is a sequence of the location
and timestamp of a moving object
Hurricanes Turtles
Vessels Vehicles
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Trajectory Classification Definition: The process of predicting the
class labels of moving objects based on their trajectories and other features
Applications: Homeland security, weather forecast, law enforcement, etc. Example: Detection of vessel
types (e.g., container ships, tankers, and fishing boats) from satellite images
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Previous Studies Several trajectory classification methods have
been proposed mainly in the fields of pattern recognition, bioengineering, and video surveil-lance
A common characteristic of earlier methods is that they use the shapes of whole trajecto-ries to do classification, e.g., by using the HMMNote: Although a few methods partition tra-jectories, the purpose of their partitioning is just to approximate or smooth trajectories
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Problem Statement and Obser-vations Problem Statement:
Given a set of labeled trajectories, generate discriminative trajectory features that make a specific class distinguishable from other classes
Observations: (1) Discriminative features are likely to appear at parts of trajectories, not at whole trajecto-ries; (2) Discriminative features appear not only as common movement patterns, but also as re-gions
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Motivating Example
Observation 1: Parts of trajectories near the container port and near the refinery enable us to distinguish be-tween container ships and tankers even if they share com-mon long paths
Observation 2: Those in the fishery enable us to recog-nize fishing boats even if they have no common path there
Re-gion
Sub-trajec-tory
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Limitations of Earlier Meth-ods
The classification accuracy of earlier methods might not be high since the overall shapes of whole trajectories are similar to each other
Þ Our framework TraClass aims at discovering both re-gion and sub-trajectory features
Overall shape
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Overall Procedure of Tra-Class Extract features in a top-down fashion,
first by region-based clustering and then by trajectory-based clustering
Region-Based Cluster-ing
Trajectory-Based Clus-tering
Trajectory partitions in non-homogeneous re-
gions
Region-based andTrajectory-based clus-
ters
Trajectory partitions
Recursively quantize non-homogeneous regions
Repeatedly find finer-granularity clusters
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Our Contributions
Achieve high classification accuracy owing to the collaboration between the two types of clustering Region features ← Region-based clustering Sub-trajectory features ← Trajectory partitioning
and trajectory-based clustering
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Where We Are Now
Region-Based Cluster-ing
Trajectory-Based Clus-tering
Trajectory partitions in non-homogeneous re-
gions
Region-based andTrajectory-based clus-
ters
Trajectory partitions
Recursively quantize non-homogeneous regions
Repeatedly find finer-granularity clusters
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Class-Conscious Trajectory Parti-tioning1. Trajectories are partitioned based on their
shapes as in the partition-and-group framework [12]
2. Trajectory partitions are further parti-tioned by the class labels The real interest here is to guarantee that tra-
jectory partitions do not span the class bound-aries
Additional partitioning points
Non-discriminative Dis-criminative
Class AClass B
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Partitioning Condition If the most prevalent class around one
endpoint is different from that around the other endpoint, further partition it Example:
Class AClass B
Prevalent class = Class A
Prevalent class = Class B
Need to be further parti-tioned
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Where We Are Now
Region-Based Cluster-ing
Trajectory-Based Clus-tering
Trajectory partitions in non-homogeneous re-
gions
Region-based andTrajectory-based clus-
ters
Trajectory partitions
Recursively quantize non-homogeneous regions
Repeatedly find finer-granularity clusters
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Region-Based Clustering Discover regions that have trajectories
mostly of one class regardless of their movement patterns The region-based cluster is a set of trajectory
partitions of the same class within a rectangular region regardless of their movement patterns(1) (2)
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Desirable Properties of Region-Based Clustering Homogeneity: The class distribution in
each region should be as homogeneous as possible
Conciseness: The number of regions should be as small as possibleNote: Two properties are contradictory to each other
Þ Need to find a good tradeoff between the properties
One large region
Many small regions
homogeneity
conciseness
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Translation into MDL Opti-mization The minimum description length (MDL) cost
consists of the description cost and the code cost The former measures conciseness, and the latter
homogeneity
The best hypothesis is the one that minimizes the sum of the description cost and the code cost
Finding a good quantization translates to find-ing the best hypothesis using the MDL principle
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Region-Based Clustering Al-gorithm Progressively find a better partitioning al-
ternately for the X axis and for the Y axis as long as the MDL cost decreases Select the partition that has the maximum code
cost and divide it into two parts in order to de-crease the MDL cost
(1) (2) (3) (4)
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Where We Are Now
Region-Based Cluster-ing
Trajectory-Based Clus-tering
Trajectory partitions in non-homogeneous re-
gions
Region-based andTrajectory-based clus-
ters
Trajectory partitions
Recursively quantize non-homogeneous regions
Repeatedly find finer-granularity clusters
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Trajectory-Based Clustering Discover sub-trajectories that indicate
common movement patterns of each class The trajectory-based cluster is a set of trajec-
tory partitions of the same class which share a common movement pattern
(3) (4)
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Trajectory-Based Clustering Al-gorithm Similar to our trajectory clustering algo-
rithm [12], but incorporate the class labels into clustering The algorithm is based on DBSCAN [5] If an ε-neighborhood contains trajectory parti-
tions mostly of the same class, it is used for clustering; otherwise, it is discarded immedi-ately
Non-homogeneous Homo-geneous ε-neighborhood ε-neigh-borhood L1 L2
X O
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Selection of Trajectory-Based Clusters After trajectory-based clusters are found,
discriminative clusters are selected for ef-fective classification If the average distance to other clusters of dif-
ferent classes is high, the discriminative power of the cluster is high
Example:
C1 C2Class AClass B
C1 is more discriminative than C2
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Generation of Cluster Links A cluster link is a sequence of connectable
(i.e., consecutive) trajectory-based clusters Two clusters are connectable if they share
enough trajectories (more formally, the ratio of common trajectories is higher than χ)
The benefit of cluster links is to derive also whole-trajectory features Cluster links are added to the set of trajectory-
based clusters for use in classification
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Classification Strategy1. Partition trajectories by considering the class la-
bels 2. Perform region-based clustering3. Perform trajectory-based clustering4. Select discriminative trajectory-based clusters5. Find cluster links from trajectory-based clusters6. Convert each trajectory into a feature vector
Each feature is either a region-based cluster or a tra-jectory-based cluster
The i-th entry of a feature vector is the frequency that the i-th feature occurs in the trajectory
7. Feed the feature vectors to the SVM
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Experimental Setting (1/2) Use three real trajectory data sets
Animal movement data set Movements of elk, deer, and cattle for the years 1993 through 1996 Three classes: Elk, Deer, and Cattle Number of trajectories (points): 38 (7117), 30 (4333), and 34 (3540)
Vessel navigation data set Navigation paths of two vessels in August 2000 Two classes: Point Lobos and Point Sur Number of trajectories (points): 600 (65500) and 550 (125750)
Hurricane track data set Atlantic Hurricanes for the years 1950 through 2006 Two classes: Category 2 and Category 3 Number of trajectories (points): 61 (2459) and 72 (3126)
Randomly select 20% of trajectories for the test set
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Experimental Setting (2/2) Measure classification accuracy, training
time, and prediction time for the three data sets
Compare two versions of the algorithm TB-ONLY: Perform trajectory-based clustering only RB-TB: Perform both types of clustering TB-ONLY is expected to be no worse than earlier
methods since it discovers also whole-trajectory features by cluster-link generation
Classification accu-racy =
# of test trajectories correctly
classified
total # of test trajectories
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Overall Results
Data Set Animal Vessel Hurricane
Version TB-ONLY
RB-TB TB-ONLY
RB-TB TB-ONLY
RB-TB
Accuracy (%) 50.0 83.3 84.4 98.2 65.4 73.1
Training Time (ms)
3542 2406 44683 22902
331 317
Prediction Time (ms)
104 98 722 608 48 46 The classification accuracy of RB-TB is much higher than that of TB-ONLY
The training time of RB-TB is much shorter than that of TB-ONLY
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Features for the Animal Data
Data: Three classes
Features:10 region-based clusters37 trajectory-based clus-ters
Red: Elk Blue: Deer Black: Cattle
Accuracy = 83.3%
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Features for the Hurricane Data
Gulf of Mex-ico
1 region-based cluster15 trajectory-based clus-ters
Red: Category 2 Blue: Cat-egory 3
Stronger hurricanes tend to go further than weaker ones
These hurricanes en-tered the Gulf of Mexico and thus stayed longer at sea before landfall than others; They are likely to get strong because hurri-canes gain energy from the evaporation of warm ocean water
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Results for Synthetic Data Effect of region-based clustering
Effect of the data size (scalability test)
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Related Work Pattern recognition [1] e.g., speech, hand-
writing, signature, and gesture recognition Classifying human motion trajectories Employing the hidden Markov model (HMM)
Bioengineering [16] Classifying biological motion trajectories
Video surveillance [15] Detecting suspicious behaviors of pedestrians
Time-series classification [20,21] Moving-object anomaly detection [14]
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Conclusions A novel and comprehensive feature genera-
tion framework for trajectories has been proposed
The primary advantage is the high classifi-cation accuracy owing to the collaboration between the two types of clustering
Various real-world applications, e.g., vessel classification, can benefit from our frame-work
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Thank You!