Framework For Segmentation and Tracking of Multiple Non Rigid Objects for Video Surveillance Aleksandar Ivanovic, Thomas S. Huang Backmen Institute for Advanced Science and Technology, UIUC
Jan 01, 2016
A Probabilistic Framework For Segmentation and Tracking of Multiple Non Rigid Objects for Video Surveillance
Aleksandar Ivanovic, Thomas S. Huang
Backmen Institute for Advanced Science and Technology, UIUC
Outline
Introduction Pixel probability model
Background model Foreground probability model
Connected components matching Object detection Experimental results Conclusion
Introduction
In video surveillance, reliable segmentation of moving objects is essential for successful event recognition
Park and Aggarwal A Gaussian mixture model is used at the pixel level
to train and classify individual pixel colors Markov Random Field (MRF) framework is used at
the blob level to merge the pixels into coherent blobs and to register inter-blob relations
A coarse model of the human body (head, upper body, lower body) is applied at the object level as empirical domain knowledge to resolve ambiguity due to occlusion and to recover from intermittent tracking failures
Introduction
Elgammal and Davis Use maximum likelihood estimation to estimate the
best arrangement for people Modeling these regions involves modeling their
appearance (color distributions) as well as their spatial distribution with respect to the body
Assumption: targets are visually isolated before occlusion so that we can initialize their models
Gomila and Meyer Each image of a sequence is segmented and
represented as a region adjacency graph Object tracking becomes a particular graph-
matching problem, in which the nodes representing the same object are to be matched
Background model
Each Lu*v* dimension is modeled with a single Gaussian
Initialize the background pixel model No foreground objects, computing the
statistics over the training sequence Otherwise, use a bootstrapping algorithm
[9] Make use of the Mahalanobis distance
to achieve segmentation, which corresponds to the probability that pixel belongs to the background
[9] Gutchess et al., “A background model initialization algorithm for video surveillance”
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Mahalanobis distance
Mahalanobis distance between each pixel and the corresponding background pixel:
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Foreground probability model (1) For each pixel , we have:
Feature vector Label (0 for background, 1 for foreground)
: absolute distance of the current pixel to the background pixel in the RGB color space
where , , , and are the means over the video frame in RGB space
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Foreground probability model (2) A color similarity measure
Computed from the cumulative histogram of all tracked objects
Computed as the number of pixels in the bin that contains divided by the number of the pixels in the color histogram
Histogram A histogram for one component (R, G, B)
describes the distribution of the number of pixels for that component color in the 16 bins
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Foreground probability model (3) Bayesian Network (BN)
Model the relationship of pixel label with feature vector
Model and using Gaussian mixture model (GMM) using Gaussians
The probability the a pixel belongs to the foreground is :
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Blob Level
This paper doesn’t mention! Foreground pixels with the same color
distribution are labeled in the same class Relabeling the disjoint blobs in
connected component analysis Adjacency Color Similarity Small blob
Components matching (1)
Find the objects in the new frame is binarized using an adaptive threshold A union find algorithm is used to find its 8-
connected components that correspond to foreground objects 4-connected components 8-connected components
Foreground object matching cases: One-to-one matching Many-to-one matching One-to-many matching
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Components matching (2)
Use probabilistic matching to solve the problems mentioned above
(foreground object, connected component): , can be described with feature vector :
: horizontal and vertical size of the bounding box
: the size in pixels : color histogram of object/component : centroid of all the pixels of an
object/connected component : index (i.e. or )
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Components matching (3)
Derive information for matching a foreground object to a connected component :
: size change as : Euclidean distance between , : similarity between , : horizontal size change as : vertical size change as
If and are matched, label with , otherwise
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Components matching (4)
Probability of matching One-to-one matching:
Occlusion :
Object color similar to background:
Probability of foreground object disappeared is , where is dummy node
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Object Detection
How to decide the connected components should become new object: Define a set of feature
: size of the connected component : distance to the nearest location of a
foreground object : a shape feature, defined as : color similarity of object candidate to the
average foreground object Label if candidate is not new object,
otherwise label
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Experimental Results
Test a 55 minute long indoor sequence
(a)(f)Foreground object
(b)Probability based only on background model
(c)Probability of foreground
(d)(g)Segmented objects using only background model
(e)(h)Segmented objects using probability of foreground
Conclusion
Contributions of this paper: A new probabilistic framework for pixel
segmentation and for matching of objects to blobs
A framework can account for grouping of objects
A method robust to initialization The matching formulation is better able
to model multi-object tracking and gives more reliable segmentation results