DDT: Decentralized event Detection and Tracking using an ensemble of vertex-reinforced walks on a graph

Abstract

Automated detection of decentralized event dynamics together with the identification of irregular topology on which the event propagates is a challenging task, which has its application in areas such as geomorphology and video surveillance. The problem becomes severe when the underlying topology is time-varying and multiple events with varied scales exist on the same topology. Conventional research works separately to deal with the problems of detecting events and identifying topology. On one hand, the methodologies for event detection involving the graph-spectral response fail to perform spatiotemporal localization of events if the underlying topology is unknown. On the other hand, the algorithms which estimate the underlying graph topology assume only static nature of the events. In this work, we utilize vertex reinforcement based walks on the topology to simultaneously perform both the tasks by using a scalable and tractable algorithm. An ensemble of such walks recursively updates the event membership of each location in the topology followed by associating a spatial support of each event. Our approach shows improvement over state-of-the-art methods in terms of the spatiotemporal localization of decentralized events.