Detecting Anomalies in Dismount Tracking Data

Abstract

This effort develops an approach for detecting behavioral anomalies using tracks of pedestrians. This research develops physically meaningful features that are understandable by an operator. The features can be used with standard classifiers such as the one class support vector machine that is used in this research. The one class support vector machine is very stable for this application and provides significant insight into the nature of its decision boundary. Its stability and ease of system use stems from a unique automatic tuning approach that is computationally efficient and compares favorably with competing approaches. This automatic tuning approach is believed to be novel and was developed as part of this research. Results are provided using hand-tracked measured video data.