Multi-Sensor Event Detection using Shape Histograms

TL;DR

This paper introduces a novel shape histogram feature for detecting events in multi-sensor vehicular time-series data, demonstrating improved accuracy over existing methods through SVM-based classification.

Contribution

It proposes a new shape histogram descriptor for variable-duration pattern detection in multi-sensor time-series, validated on vehicular data and standard datasets.

Findings

Shape histograms effectively capture variable-duration patterns.

The method outperforms existing pattern detection techniques.

Combining multiple sensors improves detection accuracy.

Abstract

Vehicular sensor data consists of multiple time-series arising from a number of sensors. Using such multi-sensor data we would like to detect occurrences of specific events that vehicles encounter, e.g., corresponding to particular maneuvers that a vehicle makes or conditions that it encounters. Events are characterized by similar waveform patterns re-appearing within one or more sensors. Further such patterns can be of variable duration. In this work, we propose a method for detecting such events in time-series data using a novel feature descriptor motivated by similar ideas in image processing. We define the shape histogram: a constant dimension descriptor that nevertheless captures patterns of variable duration. We demonstrate the efficacy of using shape histograms as features to detect events in an SVM-based, multi-sensor, supervised learning scenario, i.e., multiple time-series are used to detect an event. We present results on real-life vehicular sensor data and show that our technique performs better than available pattern detection implementations on our data, and that it can also be used to combine features from multiple sensors resulting in better accuracy than using any single sensor. Since previous work on pattern detection in time-series has been in the single series context, we also present results using our technique on multiple standard time-series datasets and show that it is the most versatile in terms of how it ranks compared to other published results.