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Updated: Apr 4, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Online Learning and Sequential Anomaly Detection in Trajectories.

Rikard Laxhammar, Göran Falkman

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 10, 2015

Summary

This summary is machine-generated.

We introduce a new method for detecting unusual movement patterns in surveillance data. This algorithm, Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD), efficiently learns from incomplete trajectory data and requires minimal parameter tuning.

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Area of Science:

Computer Science
Artificial Intelligence
Data Science

Background:

Anomalous trajectory detection is crucial for surveillance.
Existing methods struggle with incomplete data, online learning, and parameter tuning.
These limitations lead to overfitting and unreliable alarm rates.

Purpose of the Study:

To propose and evaluate the Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD).
To address limitations of existing trajectory anomaly detection algorithms.
To enable parameter-light, online learning for sequential anomaly detection.

Main Methods:

Implementation and investigation of SHNN-CAD.
Comparison with the discords algorithm.
Evaluation on four labeled trajectory datasets.

Main Results:

SHNN-CAD demonstrates competitive classification performance.
The algorithm excels in unsupervised online learning.
Effective sequential anomaly detection with minimal parameter tuning was achieved.

Conclusions:

SHNN-CAD offers a robust solution for online trajectory anomaly detection.
The parameter-light nature simplifies implementation and reduces overfitting.
SHNN-CAD provides a well-founded approach to anomaly threshold calibration.