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.
|Title of host publication||Computer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers|
|Editors||Greg Mori, Hongdong Li, C.V. Jawahar, Konrad Schindler|
|Number of pages||13|
|Publication status||Published - 2019|
|Event||14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia|
Duration: 2018 Dec 2 → 2018 Dec 6
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||14th Asian Conference on Computer Vision, ACCV 2018|
|Period||18/12/2 → 18/12/6|
Bibliographical noteFunding Information:
This work is supported in part by NSFC (No. 61672089, 61273274, and 61572064), National Key Technology R&D Program of China 2012BAH01F03, the Fundamental Research Funds for the Central Universities 2017YJS043, the NSF CAREER Grant (No. 1149783), and gifts from Adobe and Nvidia. Cong Ma and Shaoyue Song are supported by a scholarship from China Scholarship Council.
© 2019, Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)