Anomalous behavior detection in single-trajectory data

Abstract

This paper presents an original approach to dynamic anomalous behavior detection in individual trajectory using a recursive Bayesian filter. The anomalous pattern detection is of great interest for navigation, driver assistance systems, surveillance as well as crisis management. In this work, we focus on the GPS trajectories of automobiles finding where the driver's behavior shows anomalies. Such anomalous behaviors can happen in many cases, especially when the driver encounters orientation problems, i.e., taking a wrong turn, performing a detour, or losing the way. First, three high-level features, i.e., turns and their density, detour factor, and route repetition are extracted from the given trajectory geometry, for which a long-term perspective is required to observe data sequences of a significant length instead of individual time stamps. We therefore employ high-order Markov chains with a 'dynamic memory' to model the trajectory integrating these long-term features. The Markov model is processed by a proposed recursive Bayesian filter to infer an optimal probability distribution of the potential anomalous driving behaviors dynamically over time. The filter performs unsupervised detection in single trajectories based on local features only. No training process is required to characterize the anomalous behaviors. By analyzing the detection results of individual trajectories, collective behaviors can be derived indicating traffic issues such as congestions and turn restrictions. Experiments are performed on volunteered geographic information (VGI) data, self-acquired trajectories, and open trajectory datasets to demonstrate the potential of the proposed approach.

Unusual behavior detection and object ranking from movement trajectories in target regions

“…The works of Carboni and Bogorny (2014) and Huang (2015) analyze individual trajectory anomalous behavior, but they evaluate the behavior of drivers according to their trajectory movement, and not in relation to static areas as we propose in this article. Some works in trajectory analysis as Chen et al (2013) discover anomalous patterns from taxi trajectories, in order to automatically detect taxi driving frauds or road network change on real-time situations.…”

“…In GPS trajectory data analysis, which is the focus of this article, the main goal has been on extracting patterns of trajectories in relation to other trajectories (Giannotti et al 2007, de Lucca Siqueira and Bogorny 2011, Carboni and Bogorny 2014, Huang 2015. On the other hand, the behavior of moving objects in relation to points of interest (POI), such as the work of Alvares et al (2011), has not received much attention, but such discovery can reveal suspicious movements and unusual behaviors that are interesting for several application domains, mainly for security.…”

A Semantic Interpretation of Unusual Behaviors Extracted from Outliers of Moving Objects Trajectories

“…In [10], the main objective is to discover outliers among trajectories that have the same goal and move between the same regions and to give a meaning to these outliers extracted. Authors in [11] tries to extract anomalous behaviors in single-trajectory data, in [12], authors propose a method of detecting avoidance behaviors between moving objects, and the paper [13] tries to detect abnormal pedestrian behavior based on a new trajectory model, [14] and [15] are recent works that tries to detect outliers based on vehicle trajectories and multi-factors. Our work extends these works by giving a global approach which starts by merging GPS feeds with semantic data to produce semantic trajectories, then applying the mining algorithm proposed in order to give a very deeper analysis to the outliers extracted, we also try to analyze the outliers extracted according to semantic data to give more precision to the reasons for which some moving objects deviate from the main route.…”

Change Semantic Constrained Online Data Cleaning Method for Real-Time Observational Data Stream

“…The "big but dirty" real-time observational data streams can rarely achieve their full potential in the following real-time applications due to the low data quality. Therefore, a timely and meaningful online data cleaning is a necessary pre-requisite step to ensure the quality, reliability, and timeliness of the real-time observational data streams (Huang, 2015;Goodchild, 2013;PhridviRaj and GuruRao, 2014 Collective Anomaly Fig.1 The fluctuation patterns of the real-time water level data stream (Chandola et al, 2009) Anomalies in real-time observational data streams can cover a variety of different anomalous changes/events and errors and have various semantics with different length, distributions and change patterns (Chandola et al, 2009). Incorrect sensor measurements are considered as a type of anomalies in this study.…”