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Learning Object Motion Patterns for Anomaly Detection and Improved Object Detection
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| Citations: | 35 - 2 self |
BibTeX
@MISC{Basharat_learningobject,
author = {Arslan Basharat and Alexei Gritai and Mubarak Shah},
title = {Learning Object Motion Patterns for Anomaly Detection and Improved Object Detection},
year = {}
}
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Abstract
We present a novel framework for learning patterns of motion and sizes of objects in static camera surveillance. The proposed method provides a new higher-level layer to the traditional surveillance pipeline for anomalous event detection and scene model feedback. Pixel level probability density functions (pdfs) of appearance have been used for background modelling in the past, but modelling pixel level pdfs of object speed and size from the tracks is novel. Each pdf is modelled as a multivariate Gaussian Mixture Model (GMM) of the motion (destination location & transition time) and the size (width & height) parameters of the objects at that location. Output of the tracking module is used to perform unsupervised EM-based learning of every GMM. We have successfully used the proposed scene model to detect local as well as global anomalies in object tracks. We also show the use of this scene model to improve object detection through pixel-level parameter feedback of the minimum object size and background learning rate. Most object path modelling approaches first cluster the tracks into major paths in the scene, which can be a source of error. We avoid this by building local pdfs that capture a variety of tracks which are passing through them. Qualitative and quantitative analysis of actual surveillance videos proved the effectiveness of the proposed approach. 1.
Keyphrases
anomaly detection improved object detection object motion pattern scene model pixel level probability density function unsupervised em-based learning static camera surveillance anomalous event detection object detection object track quantitative analysis actual surveillance video width height global anomaly proposed method minimum object size background modelling tracking module novel framework object speed local pdfs destination location transition time scene model feedback pixel-level parameter feedback multivariate gaussian mixture model traditional surveillance pipeline pixel level pdfs object path modelling approach major path new higher-level layer
