1. Learning Trajectory Patterns by Clustering: Comparative Evaluation

2. Problem Description & Definition

3. Scene Modeling -Clustering Approaches

4. Distance/Similarity Measures
Euclidean/Modified Euclidean distance
Dynamic Time Warping
Longest Common Sequences
modified Hausdorff Distance
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5. Clustering Methods Iteration optimization Online adaptive
K-Means, Fuzzy C-Means
Online adaptive
incremental learning
Hierarchical clustering
agglomeratvie & divisive, min-cut graph based, dominant set clustering
Co-occurrence de-compostion
document and keywords
Note:
For clustering methods we decide to utilize CLUTO[7] data clustering software package, which is used for agglomerative, divisive, hybrid and graph-based clustering. The software provides a number of options and optimization criteria for each cluster variants. In matlab K-Means clustering is available to use directly.
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6. Evaluation(Dataset)
We decided to use Lankershim dataset (a total of 30 minutes vehicle trajectories processed from the video data) provided by the Federal Highway Administration (FHWA) and the Next Generation Simulation (NGSIM).
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7. Evaluation(Criteria)
We will manually label the trajectories into cluster and evaluate different distance metrics and clustering algorithms in terms of correct clustering rate (CCR)
where N is the total number of trajectories and pc denotes the total number of trajectories matched to the c-th cluster
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