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VLDB 2008. 2008-08-28 2  Motivation  TraClass: Trajectory Feature Generation  Trajectory Partitioning  Region-Based Clustering  Trajectory-Based.

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Presentation on theme: "VLDB 2008. 2008-08-28 2  Motivation  TraClass: Trajectory Feature Generation  Trajectory Partitioning  Region-Based Clustering  Trajectory-Based."— Presentation transcript:

1 VLDB 2008

2  Motivation  TraClass: Trajectory Feature Generation  Trajectory Partitioning  Region-Based Clustering  Trajectory-Based Clustering  Classification Strategy  Performance Evaluation  Related Work  Conclusions

3 Feature Generation Classifier Class label Training data Features Prediction Unseen data (Jeff, Professor, 4, ?) Tenured = Yes Scope of this paper

4  A trajectory is a sequence of the location and timestamp of a moving object Hurricanes Turtles Vessels Vehicles

5  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

6  Several trajectory classification methods have been proposed mainly in the fields of pattern recognition, bioengineering, and video surveillance  A common characteristic of earlier methods is that they use the shapes of whole trajectories to do classification, e.g., by using the HMM Note: Although a few methods partition trajectories, the purpose of their partitioning is just to approximate or smooth trajectories

7  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 trajectories; (2) Discriminative features appear not only as common movement patterns, but also as regions

8  Observation 1: Parts of trajectories near the container port and near the refinery enable us to distinguish between container ships and tankers even if they share common long paths  Observation 2: Those in the fishery enable us to recognize fishing boats even if they have no common path there Region Sub-trajectory

9  The classification accuracy of earlier methods might not be high since the overall shapes of whole trajectories are similar to each other  Our framework TraClass aims at discovering both region and sub- trajectory features Overall shape

10  Extract features in a top-down fashion, first by region-based clustering and then by trajectory- based clustering Trajectory partitions in non-homogeneous regions Region-based and Trajectory-based clusters Trajectory partitions Recursively quantize non-homogeneous regions Repeatedly find finer-granularity clusters

11  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

12 Trajectory partitions in non-homogeneous regions Region-based and Trajectory-based clusters Trajectory partitions Recursively quantize non-homogeneous regions Repeatedly find finer-granularity clusters

13 Trajectories are partitioned based on their shapes as in the partition-and-group framework [12] 2. Trajectory partitions are further partitioned by the class labels  The real interest here is to guarantee that trajectory partitions do not span the class boundaries Additional partitioning points Non-discriminative Discriminative Class A Class B

14  If the most prevalent class around one endpoint is different from that around the other endpoint, further partition it  Example: Class A Class B Prevalent class = Class A Prevalent class = Class B Need to be further partitioned

15 Trajectory partitions in non-homogeneous regions Region-based and Trajectory-based clusters Trajectory partitions Recursively quantize non-homogeneous regions Repeatedly find finer-granularity clusters

16  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)

17  Homogeneity: The class distribution in each region should be as homogeneous as possible  Conciseness: The number of regions should be as small as possible Note: Two properties are contradictory to each other  Need to find a good tradeoff between the properties One large regionMany small regions homogeneity conciseness

18  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 finding the best hypothesis using the MDL principle

19  Progressively find a better partitioning alternately 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 decrease the MDL cost (1) (2) (3) (4)

20 Trajectory partitions in non-homogeneous regions Region-based and Trajectory-based clusters Trajectory partitions Recursively quantize non-homogeneous regions Repeatedly find finer-granularity clusters

21  Discover sub-trajectories that indicate common movement patterns of each class  The trajectory-based cluster is a set of trajectory partitions of the same class which share a common movement pattern (3)(4)

22  Similar to our trajectory clustering algorithm [12], but incorporate the class labels into clustering  The algorithm is based on DBSCAN [5]  If an ε-neighborhood contains trajectory partitions mostly of the same class, it is used for clustering; otherwise, it is discarded immediately Non-homogeneous Homogeneous ε-neighborhood ε-neighborhood L1L1 L2L2 XO

23  After trajectory-based clusters are found, discriminative clusters are selected for effective classification  If the average distance to other clusters of different classes is high, the discriminative power of the cluster is high  Example: C1C1 C2C2 Class A Class B C 1 is more discriminative than C 2

24  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

25 Partition trajectories by considering the class labels 2. Perform region-based clustering 3. Perform trajectory-based clustering 4. Select discriminative trajectory-based clusters 5. Find cluster links from trajectory-based clusters 6. Convert each trajectory into a feature vector  Each feature is either a region-based cluster or a trajectory-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

26  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

27  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 accuracy = # of test trajectories correctly classified total # of test trajectories

28 Data SetAnimalVesselHurricane VersionTB-ONLYRB-TBTB-ONLYRB-TBTB-ONLYRB-TB Accuracy (%) Training Time (ms) Prediction Time (ms)  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

29 Data: Three classes Features: 10 region-based clusters 37 trajectory-based clusters Red: Elk Blue: Deer Black: Cattle Accuracy = 83.3%

30 Gulf of Mexico 1 region-based cluster 15 trajectory-based clusters Red: Category 2 Blue: Category 3 Stronger hurricanes tend to go further than weaker ones These hurricanes entered the Gulf of Mexico and thus stayed longer at sea before landfall than others; They are likely to get strong because hurricanes gain energy from the evaporation of warm ocean water

31  Effect of region-based clustering  Effect of the data size (scalability test)

32  Pattern recognition [1] e.g., speech, handwriting, 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]

33  A novel and comprehensive feature generation framework for trajectories has been proposed  The primary advantage is the high classification accuracy owing to the collaboration between the two types of clustering  Various real-world applications, e.g., vessel classification, can benefit from our framework

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