Detecting Anomalous Trajectories via Recurrent Neural Networks

Cong Ma, Zhenjiang Miao, Min Li, Shaoyue Song, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Detecting anomalies from trajectory data is an important task in video surveillance. However, it is difficult to give a precise definition of this term since trajectory data obtained from different camera views may vary in shape, direction, and spatial distribution. In this paper, we propose trajectory distance metrics based on a recurrent neural network to measure similarities and detect anomalies from trajectory data. First, we use an autoencoder to capture the dynamic features of a trajectory. The distance between two trajectories is defined by the reconstruction errors based on the learned models. We then detect anomalies based on the nearest neighbors using the proposed metric. As such, we can deal with various kinds of anomalies in different scenes and detect anomalous trajectories in either a supervised or unsupervised manner. Experiments show that the proposed algorithm performs favorably against the state-of-the-art anomaly detections on the benchmark datasets.

Original languageEnglish
Title of host publicationComputer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
EditorsGreg Mori, Hongdong Li, C.V. Jawahar, Konrad Schindler
PublisherSpringer Verlag
Pages370-382
Number of pages13
ISBN (Print)9783030208691
DOIs
Publication statusPublished - 2019 Jan 1
Event14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia
Duration: 2018 Dec 22018 Dec 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11364 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th Asian Conference on Computer Vision, ACCV 2018
CountryAustralia
CityPerth
Period18/12/218/12/6

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Ma, C., Miao, Z., Li, M., Song, S., & Yang, M. H. (2019). Detecting Anomalous Trajectories via Recurrent Neural Networks. In G. Mori, H. Li, C. V. Jawahar, & K. Schindler (Eds.), Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers (pp. 370-382). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11364 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-20870-7_23