In:
Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, Vol. 84, No. 4 ( 2018-04-01), p. 203-214
Abstract:
In this paper, we present an unsupervised learning framework for analyzing activities and interactions in surveillance videos. In our framework, three levels of video events are connected by Hierarchical Dirichlet Process ( 〈 small 〉 HDP 〈 /small 〉 ) model: low-level visual features,
simple atomic activities, and multi-agent interactions. Atomic activities are represented as distribution of low-level features, while complicated interactions are represented as distribution of atomic activities. This learning process is unsupervised. Given a training video sequence, low-level visual features are extracted based on optic flow and then clustered into different atomic activities and video clips are clustered into different interactions. The 〈 small 〉 HDP 〈 /small 〉 model automatically decides the number of clusters, i.e., the categories of atomic activities
and interactions. Based on the learned atomic activities and interactions, a training dataset is generated to train the Gaussian Process ( 〈 small 〉 GP 〈 /small 〉 ) classifier. Then, the trained 〈 small 〉 GP 〈 /small 〉 models work in newly captured video to classify interactions
and detect abnormal events in real time. Furthermore, the temporal dependencies between video events learned by HDP-Hidden Markov Models ( 〈 small 〉 HMM 〈 /small 〉 ) are effectively integrated into 〈 small 〉 GP 〈 /small 〉 classifier to enhance the accuracy of the classification in
newly captured videos. Our framework couples the benefits of the generative model ( 〈 small 〉 HDP 〈 /small 〉 ) with the discriminant model ( 〈 small 〉 GP 〈 /small 〉 ). We provide detailed experiments showing that our framework enjoys favorable performance in video event classification
in real-time in a crowded traffic scene.
Type of Medium:
Online Resource
ISSN:
0099-1112
DOI:
10.14358/PERS.84.4.203
Language:
English
Publisher:
American Society for Photogrammetry and Remote Sensing
Publication Date:
2018
detail.hit.zdb_id:
2317128-5
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