Wolfram.com
WolframAlpha.com
WolframCloud.com
Wolfram Language
Example Repository
Ready-to-use examples of the Wolfram Language
Primary Navigation
Categories
Astronomy
Audio Processing
Calculus
Cellular Automata
Chemistry
Complex Systems
Computer Science
Computer Vision
Control Systems
Creative Arts
Data Science
Engineering
Finance & Economics
Finite Element Method
Food & Nutrition
Geography
Geometry
Graphs & Networks
Image Processing
Life Sciences
Machine Learning
Mathematics
Optimization
Physics
Puzzles and Recreation
Quantum Computation
Signal Processing
Social Sciences
System Modeling
Text & Language Processing
Time-Related Computation
Video Processing
Visualization & Graphics
Alphabetical List
Submit a New Resource
Learn More about
Wolfram Language
Related Pages
Related Symbols
ImageContainsQ
HighlightImage
ImageBoundingBoxes
ImagePosition
VideoMapTimeSeries
VideoFrameList
VideoFrameMap
ImageAssemble
ImageAdd
TimeSeriesMap
Related Categories
Image Processing
Computer Vision
Video Processing
Object Recognition & Tracking in Videos
Detect and track an object in a video
Example Notebook
Open in Cloud
Download Notebook
Download the source
video
:
I
n
[
1
]
:
=
v
=
R
e
s
o
u
r
c
e
D
a
t
a
[
"
E
x
a
m
p
l
e
"
"
O
b
j
e
c
t
R
e
c
o
g
n
i
t
i
o
n
&
T
r
a
c
k
i
n
g
i
n
V
i
d
e
o
s
"
,
"
V
i
d
e
o
"
]
O
u
t
[
1
]
=
Use
I
m
a
g
e
C
o
n
t
a
i
n
s
Q
to find whether a dog is detected in each frame:
I
n
[
2
]
:
=
c
h
e
c
k
=
V
i
d
e
o
M
a
p
T
i
m
e
S
e
r
i
e
s
I
m
a
g
e
C
o
n
t
a
i
n
s
Q
#
I
m
a
g
e
,
d
o
m
e
s
t
i
c
d
o
g
C
O
N
C
E
P
T
&
,
v
;
Create a plot showing in which frames of the video a dog is detected:
I
n
[
3
]
:
=
L
i
s
t
P
l
o
t
T
i
m
e
S
e
r
i
e
s
M
a
p
[
B
o
o
l
e
,
c
h
e
c
k
]
,
o
p
t
i
o
n
s
O
u
t
[
3
]
=
Alternatively, extract the video frames and assemble a visual representation of the marked frames:
I
n
[
4
]
:
=
i
m
a
g
e
s
=
V
i
d
e
o
F
r
a
m
e
L
i
s
t
[
V
i
d
e
o
[
v
,
R
a
s
t
e
r
S
i
z
e
2
0
0
]
,
A
l
l
]
;
I
n
[
5
]
:
=
I
m
a
g
e
A
s
s
e
m
b
l
e
[
P
a
r
t
i
t
i
o
n
[
M
a
p
T
h
r
e
a
d
[
I
m
a
g
e
A
d
d
[
I
m
a
g
e
R
e
s
i
z
e
[
#
,
2
5
]
,
#
2
{
0
,
.
4
,
0
}
]
&
,
{
i
m
a
g
e
s
,
B
o
o
l
e
[
V
a
l
u
e
s
[
c
h
e
c
k
]
]
}
]
,
1
5
]
]
O
u
t
[
5
]
=
Using
I
m
a
g
e
P
o
s
i
t
i
o
n
, you can track the movement of the dog:
I
n
[
6
]
:
=
p
o
s
=
I
m
a
g
e
P
o
s
i
t
i
o
n
#
,
d
o
m
e
s
t
i
c
d
o
g
C
O
N
C
E
P
T
&
/
@
i
m
a
g
e
s
;
This is the trajectory detected in the video, over time:
I
n
[
7
]
:
=
H
i
g
h
l
i
g
h
t
I
m
a
g
e
V
i
d
e
o
E
x
t
r
a
c
t
F
r
a
m
e
s
V
i
d
e
o
v
,
.
.
.
,
,
L
i
n
e
[
F
l
a
t
t
e
n
[
p
o
s
,
1
]
]
O
u
t
[
7
]
=
Overlay the detected bounding box on the original video frame:
I
n
[
8
]
:
=
V
i
d
e
o
F
r
a
m
e
M
a
p
H
i
g
h
l
i
g
h
t
I
m
a
g
e
#
,
I
m
a
g
e
B
o
u
n
d
i
n
g
B
o
x
e
s
#
,
d
o
m
e
s
t
i
c
d
o
g
C
O
N
C
E
P
T
&
&
,
v
O
u
t
[
8
]
=
See Also
YOLO V3 Trained on Open Images Data
Scaled-YOLO V4 Trained on MS-COCO Data
Related Symbols
ImageContainsQ
HighlightImage
ImageBoundingBoxes
ImagePosition
VideoMapTimeSeries
VideoFrameList
VideoFrameMap
ImageAssemble
ImageAdd
TimeSeriesMap
Publisher Information
Contributed by:
Wolfram Staff