a probabilistic framework for segmentation and tracking of multiple non rigid objects for video...
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A Probabilistic Framework For Segmentation And Tracking Of Multiple Non Rigid Objects For
Video Surveillance
Aleksandar Ivanovic, Tomas S. HuangICIP 2004
Outline
• Introduction
• Content segmentation– Pixel probability model– Foreground probability model
• Object tracking method
• Object detection
• Experimental result
Introduction
• In video surveillance, reliable segmentation of moving objects is essential for successful event recognition.
• Tracking non-rigid objects presents several difficulties such as handling occlusion, disjoint objects and object detection.
• Park and Aggarwal proposed that the segmentation can be done on pixel, blob and object level.
Pixel Probability Model
• Use Lu*v* space
• Use a single Gaussian model the color distribution of each pixel p(x, y) at image coordinate (x, y)
• Use Mahalanobis distance Mb (x, y) for background segmentation
Foreground Probability Model
• F(x, y) : foreground label
• A(x, y) : feature vector– A(x, y) = [Mb (x, y), D(x, y), Ph (x, y))]
– D(x, y) : absolute distance• D(x, y)= |R(x, y) – Rmean(x, y)| + |G(x, y) – Gmean(x, y)|
+ |B(x, y) – Bmean(x, y)|
– Ph (x, y) : color similarity measure
Bayesian Network (BN) Modeling
Foreground Probability Model (cont.)
• P(A|F=0), P(A|F=1) :– Use Gaussian mixture model
• Gaussian mixture model :– v = [H, S, V]T, a random variable
Blob Formation
• Foreground pixels with the same color are labeled as being in the same class.
• Connected component analysis is used to relabel the disjoint blobs.– Adjacency criterion– Color similarity criterion– Small blob criterion– Skin blob criterion : especially for human
model
Connected Components Matching
• Connected components– 4-connected components– 8-connected components
• Tracking objects by matching the connected components to the foreground objects in the previous frame.– One-to-one match– Many-to-one match– One-to-Many match
Connected Components Matching (cont.)
• (f(i), c(j)) : (foreground, connected component)• k(t) : feature vector describing f(i), c(j)
– k(t) = [xs (t), ys (t), S(t), H(t), xc (t), yc (t)]• xs (t), ys (t) : horizontal and vertical size of bounding box• S(t) : size in pixel• H(t) : color histogram of object/connected component• xc(t), yc(t) : centroid of pixels of an object/connected component
• m(i, j) : information for matching f(i) to c(j)– m(i, j) = [SC(i, j), ED(i, j), HS(i, j), XC(i, j), YC(i, j)]
• SC(i, j) : size change, S(j) / S(i)• ED(i, j) : Euclidean distance between (xc(i), yc(i)) and (xc(j), yc(j))• HS(i, j) : similarity between H(i) and H(j)• XC(i, j), YC(i, j) : xs(j) / xs(i), ys(j) / ys(i)
Probability Model
• Use BN model matching from foreground object to connected components.
• M : match label (M = 1 if matched)
Probability Model (cont.)
• Case: occlusion group objects into one
• Case: similar to background match several objects at the same time
Object Detection
• The connected components not matched to any foreground object are considered to be new objects.
• Calculate the size of candidate– Doesn’t work very well with small objects
• Define feature T = [S, LC, SH, CS]
Experimental Results
d, g: segmented objects only background model
e, h: segmented objects using Pf of foreground
b: probability based only background model
c: Pf of foreground
Color Similarity Measure Ph (x, y)
• For all tracked objects: No. of pixels in bin that contains p(x, y)
Ph(x, y) = ─────────────────────────
No. of pixels in color histogram
0
20
40
60
80
100
0~15 16~31 32~47 … 240~255 value
pix
el
nu
mb
er
BN Model for Object Detection
• S : size of the connected component• LC : distance to the nearest location of an
appearance of a foreground object• SH : simple shape feature frequently used• CS : color similarity