multiple people detection and tracking with occlusion presenter: feifei huo supervisor: dr. emile a....
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Multiple People Detection and Tracking with Occlusion
Presenter: Feifei HuoSupervisor: Dr. Emile A. Hendriks Dr. A. H. J. Stijn OomesInformation and Communication Theory (ICT) GroupDelft University of Technology
Nov. 29th , 2007
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Outline Definition and objective of the project 2-D Human Model Particle Filter For People Detection and Tracking Color histogram matching Experiment Results Next
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Definition and Objective of the Project
Objective : Develop fast and robust algorithms that can detect, track,
and model accurately and robustly individual persons in the real 3D world
Challenge : Indoor scene with lighting condition changing Multi-person tracking with occlusion
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Overview of Proposed Algorithm
Foreground binary image extraction People model definition People detection and tracking using particle filter Multi-people tracking with occlusion
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Foreground Binary Image
Current Frame
Background gray image
Gray image
RGB to GRAY
Background image
RGB to GRAY
Threshold
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2-D Human Model
• The geometric properties of silhouette are used to determine if the moving objects has a human shape.
• It is convenient to describe the 2-D model mathematically, where a human hypothesis is a vector of parameters whose values are positions and size.
( ) ( )Area A Area B
( , , )P x y scale
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2-D Human Model How to use this shape feature?
(I) (II) (III)
(I) and (II)----Low Score, (III)---High Score.
Conclusion: Only when the position and scale of this shape
model fit people well, we can get high fitness value .
,1=
( ) 0, otherwise
A B if A B
Area A
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Particle Filter For Detection and Tracking A particle set is generated with an initial distribution.
Then the observation steps take place and the weights are calculated from the observation sample.
The new set of weights form the estimation to the posterior (and therefore the prior for the next iteration).
( ) ( )( )
( )
P B A P AP A B
P B
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Initialization of the detection and tracking system
Step1. Get foreground binary image
Step2. Foreground blob segmentation
Step3. Size filter to get candidate blob with people
Step4. Initialize particle filter with Gaussian distribution
Step1 Step2 Step3 Step4
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Particle filter for people detection and tracking
Iteration=20
Initial Frame
Particle system Detection result
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Multiple people tracking with head model
• After the convergence of the head-should-upper body model, we can set the nominal scale of the head model for tracking people.
• Head model can provide more accurate position and scale information of the people.
Iteration
( ) ( )Area A Area B
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Multiple people tracking with occlusion
Demo2
Demo1
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Use discriminative feature to identify different people
• Objective:
1. Find out whether person A occludes person B, or the other way
around. 2. A group of people detection and tracking.
• Solution:
1. Use color information to distinguish different people.
2. The parameters of 2-D human model are increased into positions,
size and color. P=(x, y, scale, color)
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Color histogram similarity
1. Initialize color histogram before occlusion.
2. Calculate color histogram similarity when occlusion.
3. Identify individual people after occlusion.
16 16 16
1...16 16 161
ˆ ˆ ˆq= 1, , ,c cu uu
u
q q c R G B
Demo3
16 16 16
1...16 16 161
ˆ ˆ ˆp(y)= (y) 1, , ,c cu uu
u
p p c R G B
16 16 16
1
ˆ ˆ ˆ ˆ ˆ(y) [p(y),q]= (y) , , ,c cu u
u
p q c R G B
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To be continued
1. Evaluation of the algorithm2. Testing with different videos
Objective:1. Optimize the parameters in the algorithm. 2. Increase the implementation speed.
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Thanks for your attention !
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