Similarity based vehicle trajectory clustering and anomaly detection

By Zhouyu Fu, Weiming Hu, Tien-Ping Tan. IEEE International Conference on Image Processing 2005, 2005

318 citations recorded by the source corpus.

How researchers see this paper

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Detecting anomalous events in videos by learning deep representations of appearance and motion (2017, background): “Early works in the literature are based on the analysis of individual moving objects in the scene [5, 6, 7, 8].”
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications (2018, methodology): “, one-class SVM [1, 7], clustering based methods [9] like K-Means [26] and GMM [21], KDE [27], and VAE [2] and VRNN [33].”
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection (2015, methodology): “By using accurate tracking algorithms, trajectory extraction can be carried out to further perform trajectory clustering analysis [5, 20] or design representative features [28] to model typical activities and subsequently discover anomalies.”
Intelligent multi-camera video surveillance: A review (2013, background): “Many approaches (Keogh and Pazzani, 2000; Makris and Ellis, 2002; Porikli, 2003; Junejo et al., 2004; Fu et al., 2005; Zhang et al., 2006; Wang et al., 2011) have been proposed to cluster trajectories of objects into different activity categories without supervision.”