کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
527760 869355 2013 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions
چکیده انگلیسی


• A fast and accurate algorithm for anomalous event detection in videos.
• Densely sampling video to construct spatio-temporal video volumes.
• Handling uncertainties in codebook construction.
• Coding spatio-temporal composition of volumes by a probabilistic framework.
• Unsupervised and continuously learning new events.

This paper presents an approach for detecting suspicious events in videos by using only the video itself as the training samples for valid behaviors. These salient events are obtained in real-time by detecting anomalous spatio-temporal regions in a densely sampled video. The method codes a video as a compact set of spatio-temporal volumes, while considering the uncertainty in the codebook construction. The spatio-temporal compositions of video volumes are modeled using a probabilistic framework, which calculates their likelihood of being normal in the video. This approach can be considered as an extension of the Bag of Video words (BOV) approaches, which represent a video as an order-less distribution of video volumes. The proposed method imposes spatial and temporal constraints on the video volumes so that an inference mechanism can estimate the probability density functions of their arrangements. Anomalous events are assumed to be video arrangements with very low frequency of occurrence. The algorithm is very fast and does not employ background subtraction, motion estimation or tracking. It is also robust to spatial and temporal scale changes, as well as some deformations. Experiments were performed on four video datasets of abnormal activities in both crowded and non-crowded scenes and under difficult illumination conditions. The proposed method outperformed all other approaches based on BOV that do not account for contextual information.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Vision and Image Understanding - Volume 117, Issue 10, October 2013, Pages 1436–1452
نویسندگان
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