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Presented at the 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing (ICCP 2011), August 26th, 2011 in Cluj-Napoca, Romania.
Publication: http://bit.ly/x1OpFL
Abstract:
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During my Ph.D. in mechanical engineering / robotics, I developed a kinematostatic and a quasi-static model of compliant parallel mechanisms. These models are general and valid for any kind of mechanisms.
In this work, I also developed a general formulation of the sitffness matrix of a parallel mechanism.
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Based on the preprint http://arxiv.org/abs/quant-ph/0509193
Talk was recorded and is viewable online at http://pirsa.org/05070102/
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Presented at the 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing (ICCP 2011), August 26th, 2011 in Cluj-Napoca, Romania.
Publication: http://bit.ly/x1OpFL
Abstract:
In this paper we introduce a system for semantic understanding of traffic scenes. The system detects objects in video images captured in real vehicular traffic situations, classifies them, maps them to the OpenCyc1 ontology and finally generates descriptions of the traffic scene in CycL or cvasi-natural language. We employ meta-classification methods based on AdaBoost and Random forest algorithms for identifying interest objects like: cars, pedestrians, poles in traffic and we derive a set of annotations for each traffic scene. These annotations are mapped to OpenCyc concepts and predicates, spatiotemporal rules for object classification and scene understanding are then asserted in the knowledge base. Finally, we show that the system performs well in understanding traffic scene situations and summarizing them. The novelty of the approach resides in the combination of stereo-based object detection and recognition methods with logic based spatio-temporal reasoning.
During my Ph.D. in mechanical engineering / robotics, I developed a kinematostatic and a quasi-static model of compliant parallel mechanisms. These models are general and valid for any kind of mechanisms.
In this work, I also developed a general formulation of the sitffness matrix of a parallel mechanism.
Nondeterministic testing of Sequential Quantum Logic Propositions on a Quant...Matthew Leifer
Talk given at "Quantum Information, Computation and Logic" workshop at Perimeter Institute in 2005.
Based on the preprint http://arxiv.org/abs/quant-ph/0509193
Talk was recorded and is viewable online at http://pirsa.org/05070102/
I now regard these ideas as flawed, but they may be revisited in future work.
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CASA workshop 3AMIGAS (supported by FOCUS K3D and GATE)
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presenter Feifei Huo, TU Delft
http://www.cs.uu.nl/events/3amigas/
http://www.focusk3d.eu/
http://gate.gameresearch.nl
Technical presentation of the gesture based NUI I developed for the Aigaio smart conference room in IIT Demokritos
Demo In Greek:
https://www.youtube.com/watch?v=5C_p7MHKA4g
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https://alandix.com/academic/papers/synergy2024-epistemic/
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golf
1. Robust Visual Golf Club
Tracking
Vincent Lepetit, Nicolas Gehring, Pascal Fua
CVlab - EPFL
2. Aim
1.5 sec
• Completely automatic video based system.
– No user intervention.
– Use of a single video (PAL) camera.
– No external expensive devices.
– No specifically instrumented golf clubs or clothing.
• Usable in natural environments
– Cluttered (fixed) background 2
3. Club: thin, specular reflexion, moves very fast
(up to 170km/h).
1
t + sec
t
25
3
4. • Club extraction
• Tracking algorithm with
– local motion model
– global motion model
4
5. Dealing with interlaced images
• For each frame:
2 « half-images » from an interlaced image
50 « half-images » per second 5
6. Detection of moving objects
Frame n-1
- Threshold +
Closing
Frame n Mask of the
moving objects in
AND
frame n
- Threshold +
Closing
Frame n+1
6
7. Hypothesis generation
Detection of adjacent parallel segments under the moving-object mask
1. Edges detection
2. Segment detection (contour extraction, polygonal approximation)
3. Parallel segment detection and fusion
7
9. Hypothesis generation
• The resulting segment is only a part of the shaft
Search for the shaft end-points
• Looking for the club • Looking for the “hands” in
head in the moving- the color image
object mask (as the last
white point)
9
11. Hypothesis generation
• Heuristics for removing some hypotheses
– Given a 2D point somewhere between the golfer shoulders,
we can remove some physically impossible hypotheses
Shoulders
Shoulders
Possible Impossible
• No accurate position for this point needed
• Can be easily provided by the user
11
12. Why we still need a tracking algorithm ?
• Some wrong hypotheses can not be removed:
?
?
?
?
12
13. Tracking
• Many visual tracking techniques have been
proposed in the computer vision literature.
– Data Association approaches (MHT)
– ConDensation
– Based on recursive motion models: Xt+1 = f(Xt)
– Difficult to consider a specific motion such as a golf
swing.
– Suffer from a lack of robustness for practical
applications when:
• Frequent mis-detections
• Large acceleration
• Abrupt motion changes
13
14. New tracking algorithm
• Idea:
– Take into account previous frames + next
frames
– Consider the detections in these frames to
locally estimate the club shaft motion
14
15. New algorithm
MLESAC applied to tracking:
• Choose randomly 3 frames (in the previous and next frames)
• Choose randomly one detection in these frames
• Compute the shaft motion assuming a locally constant
acceleration
• Estimate the shaft position in the previous and next frames
Several examples:
• Compute the support of the predicted motion i.e. the number of
frames where there is a detection near the predicted position
• Repeat and keep the shaft motion with the maximum likelihood
Deals easily with mis-detections and false-alarms
15
Robust motion estimation
16. Motion estimation
• Parametrisation of the shaft (double-pendulum model):
ϕ
s = [Shoulders, L, R, Ψ, ϕ] L
R
ψ
• Estimation
– From the three randomly selected shafts si, sj, sk,
– assuming a constant acceleration for all the parameters,
• we can predict the position, velocity and acceleration of the shaft
s 0 = [Shoulders 0, L 0, R 0, Ψ 0, ϕ 0] in the current frame:
i (i − 1)
Ψ0 Ψi
1. i
2
j ( j − 1)
Ψ0 = A−1 Ψ j
A = 1. j
2
k (k − 1)
Ψ0 Ψk
1. k
2
• we can also predict the position in the other frames:
n(n − 1)
Ψn = Ψ0 + nΨ0 + Ψ0
2 16
…
17. Maximum Likelihood Estimation
• Mt motion at time t
• Zt ={zt-nB … zt+nA} detection sets for frames t-nB to t+nA
M t = arg max p( Z t | M )δ ( M )
M
~
max p( Z t | M S )δ ( M S )
• Random sampling: M = argM
S
is an initial estimate of Mt, and refined using all the correct
detections
+ nA
p ( Z t | M S ) = ∏ p ( z t + i | yt + i , S )
i = nB
Classical observation model
17
18. Advantages
• The shaft position can be estimated when it is not
detected, with very good accuracy:
• Using the next frames makes the tracker:
– More robust
– More accurate
18
– Almost Automatic!