US2014254875A1PendingUtilityA1
Method and system for automatic objects localization
Est. expiryMay 13, 2030(~3.8 yrs left)· nominal 20-yr term from priority
G06V 20/52G06V 20/00H04N 23/90G06V 20/53G06V 20/54G06T 2207/30196G06T 2207/10004G06T 2207/30232G06T 2207/10016H04N 7/18G06T 7/0079G06T 2207/20144H04N 5/33G06K 9/00771H04N 5/247G06K 9/00778G06T 7/0044
43
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Claims
Abstract
A method for automatic localization of objects in a mask. The method includes building a dictionary or atoms, wherein each atom models the presence of one object at one location and iteratively determining the atom of said dictionary which is best correlated with said mask, until ending criteria are met. The invention system concerns also automatically detects objects in a mask. At least one fixed camera is provided for acquiring video frames. A computation device is used for calibrating at least one fixed camera for extracting foreground silhouettes in each acquired video frames for discretizing said ground plane into a non-regular grid of potential location points for constructing a dictionary of atoms, and for finding objects location points with the previous method. And a propagating device is provided to propagate the result in at least one fixed camera view.
Claims
exact text as granted — not AI-modified1 . A method for automatic localization of objects in a mask, comprising the steps of:
building a dictionary of atoms, wherein each atom models the presence or one object at one location; iteratively determining the atom of said dictionary which is most correlated with said mask, until ending criteria are met.
2 . The method of claim 1 , wherein said mask is computed by a foreground-background extraction process from an image acquired by at least one camera,
and wherein each step comprises the determination of the atom which is most correlated with said mask, and the adaptation or a remainder by taking out said atom.
3 . The method of claim 2 wherein said mask is a binary mask.
4 . The method of claim 1 , wherein said dictionary comprises a list of atoms at each of a plurality of uneven spaced positions.
5 . The method of claim 1 , wherein each atom models the different images taken by a plurality of different cameras of one object at one location.
6 . The method of claim 1 , wherein said method recovers an occupancy vector according to the formula
x
^
=
argmin
x
(
W
H
(
y
⊕
D
·
x
)
)
with the constraint of minimizing the number of elements of said occupancy vector different to zero and the constraint W H (x)≦k, where x is said occupancy vector, y is a multi-silhouette vector, D is the dictionary of atoms, ⊕ is a bitwise XOR operator, W H (·) is the Hamming weight of Boolean vector and k is an integer and positive number.
7 . The method or claim 1 , wherein said method recovers an occupancy vector according to the formula
x
^
=
argmin
x
(
W
H
(
x
)
)
with the constraint or minimizing the number of elements of said occupancy vector different to zero and the constraint W H (y⊕D·x)≦ε, where x is said occupancy vector, y is a multi-silhouette vector, D is the dictionary of atoms, ⊕ is a bitwise XOR Operator, W H (·) is the Hamming weight of a Boolean vector and ε is an integer and positive number.
8 . The method of claim 1 , wherein said method recovers an occupancy vector according to the formula
{circumflex over (x)}= argmin(α W H ( x )+ W H ( y⊕D·x ))
with the constraint or minimizing the number or elements or said occupancy vector different to zero, where x is said occupancy vector, y is a multi-silhouette vector, D is the dictionary or atoms, ⊕ is a bitwise XOR operator, W H (·) is the Hamming weight of a Boolean vector and α is a regularization parameter.
9 . The method of claim 1 comprising
a) selecting the most correlated atom or said dictionary or atoms with said multi-silhouette vector for each possible location
c) updating a remainder of said multi-silhouette vector taking out the contribution of said most correlated atom
e) repeating steps a) to c) until meeting ending criteria.
10 . The method or claim 9 wherein said ending criteria depend on an a priori knowledge of the number or objects to be detected.
11 . The method or claim 9 wherein said ending criteria depend on an upper bound error level or the energy level or said remainder.
12 . The method of claim 9 wherein said ending criteria depend on the decreasing of an error level e.
13 . The method of claim 9 wherein said most correlated atom corresponds to a maximal statistic.
14 . The method of claim 13 wherein said maximal statistic is the sum of two parameters depending on said atoms, said remainder, said multi-silhouette vector and a second regularization factor.
15 . The method of claim 14 wherein the first parameter of said two parameters is the cover defined by the formula
W
H
(
d
j
′
⋀
R
)
W
H
(
R
)
wherein d 1 are said atoms, R is said remainder, is the bitwise AND operator and W H (·) is the Hamming weight of a Boolean vector.
16 . The method of claim 14 wherein the first parameter of said two parameters is the cover defined b the formula
W
H
(
d
j
′
⋀
y
)
W
H
(
d
j
′
)
wherein d j′ are said atoms, y is said multi-silhouette vector, is the bitwise AND operator and W H (·) is the Hamming weight of a Boolean vector.
17 . The method of claim 14 wherein the first parameter of said two parameters is the cover defined by the formula
W
H
(
d
j
′
⋀
(
R
⋁
y
^
)
)
W
H
(
d
j
′
)
wherein d j′ are said atoms, R is said remainder, ŷ is a recovered multi-silhouette vector, is the bitwise AND operator and W H (·) is the Hamming weight of a Boolean vector.
18 . The method of claim 14 wherein the second parameter of said two parameters is the fitness defined by the formula
W
H
(
d
j
′
⋀
R
)
W
H
(
d
j
′
)
wherein d j′ are said atoms, R is said remainder, is the bitwise AND operator and W H (·) is the Hamming weight of a Boolean vector.
19 . The method of claim 1 , comprising a preprocessing step and a main greedy process step.
20 . The method of claim 19 , wherein said preprocessing step reduces the search space of said main greedy process step.
21 . The method of claim 1 wherein said atoms in said dictionary depend on the shape of said objects.
22 . The method of claim 1 wherein said dictionary depends on the position, the zoom, the focus and the resolution of said at least one camera.
23 . The method of claim 1 wherein said location is defined by an adaptive discretization or a ground.
24 . The method of claim 23 wherein said adaptive discretization comprises the mapping of points regularly spaced in an image plane of said at least one camera to samples points of said ground and a quantization of said samples points on said ground.
25 . The method of claim 1 wherein said objects are people in a crowded environment.
26 . The method of claim 25 , wherein the method select an atom that has a minimum distance with previous selected atoms.
27 . The method of claim 26 , wherein said minimum distance is comprised between 60 cm and 70 cm.
28 . The method of claim 25 wherein a half-cylinder-half spherical shape is used to approximate the silhouette of a person in a view of said at least one camera.
29 . The method of claim 1 wherein there is at least two cameras.
30 . The method of claim 24 wherein said dictionary is a matrix wherein the number of rows corresponds to the resolution of said at least one camera and the number of columns corresponds to the number of said samples points multiplied by the number of possible shapes of said atoms.
31 . The method of claim 24 wherein said adaptive discretization is a function of the topology of said cameras and of the activity of the scene.
32 . The method of claim 24 comprising the measuring the activity of said samples points according to said assumption:
Sample points are ground plane points belonging to the foreground pixels of at least one camera.
33 . The method of claim 24 comprising the measuring the activity of said samples points according to said assumption
Sample points are ground plane points belonging to the foreground pixels of all the cameras observing the corresponding points.
34 . The method of claim 24 comprising the measuring the activity of said samples points according to said assumption
Sample points are ground plane points corresponding to a significant foreground silhouette in all the cameras observing the corresponding points.
35 . The method of claim 1 comprising
a. acquiring a multi-silhouette vector y
b. defining an upper-hound on the number of the objects W H (x)≦k
c. defining a regularization factor λ
d. defining a dictionary D
e. creating an output support set {circumflex over (Λ)}
f. Initializing
{circumflex over (Λ)}={ }, U={ }, R y, ŷ 0, e W H (y), t 1
g. performing a preprocessing step for reducing the search space to a set U⊂{1, 2, . . . , N}
h. computing the sequence of statistics according to the formula
λ
W
H
(
d
j
′
⋀
R
)
W
H
(
d
j
′
)
+
(
1
-
λ
)
W
H
(
d
j
′
⋀
R
)
W
H
(
R
)
j
′
∈
U
wherein d j′ are said atoms, R is said remainder, is bitwise AND operator, W H (·) is the Hamming weight of a Boolean vector and λ is a regularization factor,
i. repeating the step h, for each point between the number of points on the search space
l. finding the argmax of said sequence of statistics
m. updating said output support set according to the formula {circumflex over (Λ)} {circumflex over (Λ)}∪{j}
n. updating said recovered multi-silhouette vector according to the formula ŷ ŷ d 1 , where d is the atom corresponding to said argmax of said sequence of statistics
o. updating said remainder according to the formula R R ( ŷ) Wherein is the bitwise NOT operator
p. updating an error according to the formula e W H (ŷ⊕y), wherein ⊕ is the bitwise XOR operation between vectors
q. updating a counter according to the formula t t+1
r. repeating steps h. to q until t≦k.
36 . The method of claim 1 comprising
a. acquiring a multi-silhouette vector y
b. defining an upper-bound on the noise level W H (x)≦ε
c. defining a regularization factor λ
d. defining a dictionary D
e. creating an output support set {circumflex over (Λ)}
f. initializing
{circumflex over (Λ)}={ }, U={ }, R y, ŷ 0, e W H (y), t 1
g. performing a preprocessing step for reducing the search space to a set U⊂{1, 2, . . . , N}
h. computing the sequence of statistics according to the formula
λ
W
H
(
d
j
′
⋀
R
)
W
H
(
d
j
′
)
+
(
1
-
λ
)
W
H
(
d
j
′
⋀
R
)
W
H
(
R
)
j
′
∈
U
wherein d 1 are said atoms, R is said remainder, is the bitwise AND operator, W H (·) is the Hamming weight of a Boolean vector and λ is a regularization factor,
i. repeating the step h. for each point between the number of points on the search space
l. finding the argmax of said sequence of statistics
m. updating said output support set according to the formula {circumflex over (Λ)} {circumflex over (Λ)}∪{j}
n. updating said recovered multi-silhouette vector according to the formula ŷ ŷ d 1 , where d is the atom corresponding to said argmax of said sequence of statistics
o. updating said remainder according to the formula R R ( ŷ) wherein is the bitwise NOT operator
p. updating an error according to the formula e W H (ŷ⊕y), wherein ⊕ is the bitwise XOR operator between Boolean vectors
q. updating a counter according to the formula t t+1
r. repeating steps h. to q. until e>ε.
37 . The method of claim 1 comprising
a. acquiring a multi-silhouette vector y
b. defining a regularization parameter α
c. defining ti regularization factor λ
d. defining a dictionary D
e. creating an output support set {circumflex over (Λ)}
f. initializing
{circumflex over (Λ)}={ }, U={ }, R y, ŷ 0, e W H (y), e p W H (y)+α, t 1
g. performing a preprocessing step for reducing the search space to a set U⊂{1, 2, . . . , N}
h. computing the sequence of statistics according to the formula
λ
W
H
(
d
j
′
⋀
R
)
W
H
(
d
j
′
)
+
(
1
-
λ
)
W
H
(
d
j
′
⋀
R
)
W
H
(
R
)
j
′
∈
U
wherein d j′ are said atoms, R is said remainder, is the bitwise AND operator, W H (·) is the Hamming weight of a Boolean vector and λ is the regularization factor,
i. repeating the step h. for each point between the number of points on the search space
l. Finding the argmax of said sequence of statistics
m. updating said output support set according to the formula {circumflex over (Λ)} {circumflex over (Λ)}∪{j}
n. updating said recovered multi-silhouette vector according to the formula ŷ ŷŷ d, where d is the atom corresponding to said argmax of said sequence of statistics
o. updating said remainder according to the formula R R ( ŷ) wherein is the bitwise NOT operator
p. updating an error according to the formula e W H (ŷ⊕ŷ), wherein ⊕ is the bitwise XOR operator between Boolean vectors
q. updating a counter according to the t t+1
r. repeating steps h. to q. until e p −e>α.
38 . A method for automatic localization of objects in an image taken by at least one fixed camera acquiring a multi-silhouette vector wherein said method recovers an occupancy vector by using said multi-silhouette vector and a dictionary of atoms, each atom modeling the presence of a single object at a given location of said image.
39 . The method of claim 38 , comprising a plurality of iterative steps, wherein at each of said iterative step the atom of said dictionary that best matches said image is determined.
40 . The method of claim 38 , which takes into account a sparsity constraint, i.e. the constraint or minimizing the number of non-zero elements of said occupancy vector x.
41 . A non-transitory computer readable medium storing a program causing a computer to execute instructions executable to compute the method of claim 1 .
42 . A system for automatically detecting objects in a mask, comprising
at least one fixed camera for acquiring video frames; computation means for calibrating said at least one fixed camera; computation means for extracting foreground silhouettes in each acquired video frames; computation means for discretizing said ground plane into a non-regular grid of potential location points; computation means for constructing a dictionary of atoms, each atom modeling the presence or a single object at a given location of said ground plane; computation means for finding, objects location points with the method of claim 1 ; means for propagating the result in said at least one fixed camera view.
43 . The system of claim 42 , wherein said mask is computed by a foreground-background extraction process from an image acquired by at least one camera.
44 . The system of claim 42 comprising at least two cameras.
45 . The system of claim 42 wherein said at least one camera is a planar and/or omnidirectional camera.
46 . The system of claim 42 wherein said at least one camera is an IR camera.
47 . The system of claim 44 wherein said cameras have overlapping field-of-views.
48 . The system of claim 42 wherein said objects are people in a crowded environment.
49 . The system or claim 42 wherein all said computation means belong to an image processing system.Join the waitlist — get patent alerts
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