Abstract:
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Modeling and recognizing human behaviors in a visual surveillance task is receiving
increasing attention from computer vision and machine learning researchers. Such a system
should deal in particularly with detecting when interactions between people occur and
classifying the type of interaction.
In this work we study a flexible model for detecting human interactions. This has
been done by detecting the people in the scene and retrieving their corresponding pose and
position sequentially in each frame of the video. To achieve this goal our work relies on
robust object detection algorithm which is based on discriminatively trained part based
models to detect the human bodies in videos. We apply a ‘Gaussian Mixture Models based’
method for background subtraction and human segmentation. The output from the
segmentation method which is labeled human body is combined with the background
subtraction methods to obtain a bounding box around each person in images to improve the
task of human body pose detection.
To gain more precise pose detection models, we trained the algorithm on large,
challenging but reliable dataset (PASCAL 2010). Our method is applied in home-made
database comprising depth data from Kinect sensors. After successfully getting in every
image sequence the corresponding label for each person as well as their pose and position,
understanding of human motion comes naturally which is an important step towards human
interaction analysis. |