Authors:
Jorge Henrique Busatto Casagrande
1
and
Marcelo Ricardo Stemmer
2
Affiliations:
1
Instituto Federal de Santa Catarina, Brazil
;
2
Universidade Federal de Santa Catarina and DAS/UFSC, Brazil
Keyword(s):
Abnormal Motion Detection, Video Analysis, Automated Surveillance, Motion Analisys, Pattern Recognition.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Image and Video Analysis
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Software Engineering
;
Video Analysis
Abstract:
This article proposes a method to detect abnormal motion based on the subdivision of regions of interest
in the scene. The method reduces the large amount of data generated in a tracking-based approach as well
as the corresponding computational cost in training phase. The regions are spatially identified and contain
data of transition vectors, resulting from the centroid tracking of multiple moving objects. On these data, we
applied a one-class supervised training with one set of normal tracks on Gaussian mixtures to find relevant
clusters, which discriminate the trajectory of objects. The lowest probability of transition vectors is used as
the threshold to detect abnormal motions. The ROC (Receiver Operating Characteristic) curves are used to
this task and also to determinate the efficiency of the model for each size increment of the region grid. The
results show that there is a range of grid size values, which ensure a best margin of correct abnormal motions
detection for each type
of scenario, even with a significant reduction of data samples.
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