Hierarchical Matching of 3D Pedestrian Trajectories for Surveillance Applications

Abstract

In this paper we propose a string-based approach to effectively represent trajectories in the 3D space. The strategy is coupled with a syntactical matching algorithm that allows evaluating the similarity of the retrieved data with pre-stored templates. The symbolic representation of the trajectory, is the core of the proposed system, which helps discriminating among different tracks using a modified version of the edit-distance. The hierarchical application of the algorithm on the spatial and temporal components helps detecting anomalous trajectories, and has proven to be robust in automatically learning new instances or classes of paths. We present the results achieved by performing a number of tests in an indoor lab used as a testbed for assisted living applications. The algorithm can discriminate among different classes of trajectories and can recognize actions and detect anomalies within the same class.

Survey of maps of dynamics for mobile robots

“…The research directions related to changes and dynamics have received and are still receiving a substantial amount of attention within the computer vision (CV) community. The research topics include, but are not limited to surveillance and anomaly detection (Acevedo-Rodríguez et al, 2011; Anjum and Cavallaro, 2008; Choong et al, 2014; KamaliArdakani et al, 2017; Liu et al, 2014b; Owens and Hunter, 2000; Piciarelli and Foresti, 2006; Piotto et al, 2009; Piciarelli et al, 2005; Rodríguez-Serrano and Singh, 2012; Santhosh et al, 2019; Siang and Wang Khor, 2012; Sun et al, 2017; Shu-Yun and Huang, 2010; Weiming et al, 2004, 2006; Zhouyu et al, 2005), activity recognition (Anjum and Cavallaro, 2007; Atev et al, 2006; Khan et al, 2016; Morris and Trivedi, 2011; Nawaz et al, 2014; Zhang et al, 2009), crowd analysis (Cheriyadat and Radke, 2008; Khan et al, 2016; Sharma and Guha, 2016; Zhou et al, 2011), and appearance change (Lowry et al, 2016).…”