Anomalous human behavior detection: an adaptive approach

Coen van Leeuwen; Arvid Halma; Klamer Schutte

doi:10.1117/12.2015678

23 May 2013 Anomalous human behavior detection: an adaptive approach

Coen van Leeuwen, Arvid Halma, Klamer Schutte

Author Affiliations +

Proceedings Volume 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII; 874519 (2013) https://doi.org/10.1117/12.2015678
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Detection of anomalies (outliers or abnormal instances) is an important element in a range of applications such as fault, fraud, suspicious behavior detection and knowledge discovery. In this article we propose a new method for anomaly detection and performed tested its ability to detect anomalous behavior in videos from DARPA's Mind's Eye program, containing a variety of human activities. In this semi-unsupervised task a set of normal instances is provided for training, after which unknown abnormal behavior has to be detected in a test set. The features extracted from the video data have high dimensionality, are sparse and inhomogeneously distributed in the feature space making it a challenging task. Given these characteristics a distance-based method is preferred, but choosing a threshold to classify instances as (ab)normal is non-trivial. Our novel aproach, the Adaptive Outlier Distance (AOD) is able to detect outliers in these conditions based on local distance ratios. The underlying assumption is that the local maximum distance between labeled examples is a good indicator of the variation in that neighborhood, and therefore a local threshold will result in more robust outlier detection. We compare our method to existing state-of-art methods such as the Local Outlier Factor (LOF) and the Local Distance-based Outlier Factor (LDOF). The results of the experiments show that our novel approach improves the quality of the anomaly detection.

Citation Download Citation

Coen van Leeuwen, Arvid Halma, and Klamer Schutte "Anomalous human behavior detection: an adaptive approach", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 874519 (23 May 2013); https://doi.org/10.1117/12.2015678

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