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Learning Trajectory Patterns by Clustering: Comparative Evaluation

This study evaluates various clustering approaches for learning trajectory patterns using different distance measures, clustering methods, and evaluation criteria. It utilizes the CLUTO software package for data clustering and the Lankershim dataset for evaluation.

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Learning Trajectory Patterns by Clustering: Comparative Evaluation

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  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

  5. Clustering Methods Iteration optimization • 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.

  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).

  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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