Authors:
Luis Rodriguez-Benitez
1
;
Juan Moreno-Garcia
2
;
Javier Albusac
3
;
Jose Jesus Castro-Schez
3
and
Luis Jimenez
3
Affiliations:
1
Escuela Universitaria Politecnica, Universidad de Castilla-La Mancha, Spain
;
2
Escuela de Ingeniera Tecnica Industrial, Universidad de Castilla-La Mancha, Spain
;
3
Escuela Superior de Informatica, Universidad de Castilla-La Mancha, Spain
Keyword(s):
Segmentation and Grouping, Motion and Tracking, Approximate Reasoning, MPEG compressed domain.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
In this work we present a system that describes linguistically the position of an object in motion in each frame of a video stream. This description is obtained directly from MPEG motion vectors by using the theory of fuzzy sets and approximate reasoning. The lack of information and noisy data over the compressed domain justifies the use of fuzzy logic. Besides, the use of linguistic labels is necessary since the system’s output is a semantic description of trajectories and positions. Several methods of extraction of motion information from MPEG motion vectors can be found in the revised literature. As no numerical results are given of these methods, we present a statistical study of the input motion information and compare the output of the system depending on the selected extraction technique. For system performance evaluation it would be necessary to determine the error between the semantic output and the desired object’s description. This comparison is carried out between the (x,
y) pixel coordinates of the center position of the object and the resulting value of a defuzzification method applied to the description labels. The system has been evaluated using three different video samples of the standard datasets provided by several PETS (Performance Evaluation of Tracking and Surveillance) workshops.
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