1. Instructor: Deborah Cohen
Students: Alon Stolero & Amir Cohen

2. Background – Project goal
 The project is a real time object tracking based on 2 novel
approaches of 2 different algorithms: TLD and STRUCK
 The new approaches are about the prediction of the object`s
location and learning the object with the changes in the video
stream, this kind of learning is called long-term-tracking
 The project will eventually be implemented on a BeagleBoard©
platform in order to fit it to embedded systems
 In previous approaches the correlation had been tested by a
“running” bounding box along the frame to find the best
match, the problem was to adjust to changes made in the
object

3. The algorithm description and goal
 The algorithm`s goal is real time object tracking while
adjusting to changes in the object
 The algorithm receive as input bounding box
surrounding the chosen object in the frame
 The algorithm`s output is a video stream with a
bounding box around the chosen object
 In addition, the algorithm supposed to be sensitive to
changes in the object`s distance and it`s angle from
the viewer

4. STRUCK algorithm flow
Receiving inputs:
𝑓𝑡, 𝑝𝑡−1, 𝑠𝑡−1
Estimating change
in object location
Calculating the
transformation on time t by
finding the most probable
transformation based on
SVM using eq.:
𝑦𝑡 = arg max 𝐹 𝑥 𝑡
𝑝 𝑡−1
, 𝑦
𝑓𝑡 = the current frame
𝑝𝑡−1= the last bbox
𝑠𝑡−1= the transformation similarity function
𝑦 = the options space for the next transformation
𝑥 𝑡 = the sampling in time t, the pattern

5. STRUCK algorithm flow
In order to find the
new bbox for time t,
the algorithm use:
𝑝𝑡 = 𝑝𝑡−1 ∘ 𝑦𝑡
Updating the
discriminant
function F with the
new position, for the
next SVM vector set
While adjusting the SVM
the algorithm uses Budget-
Maintenance function to
limit the number of vectors
for the SVM
Receiving the new
samples in time t
and checks it`s
relevance to the
object using
previous samples
Going over all the
“old” (stored)
samples and
optimizing them
Returning 𝑝𝑡, 𝑠𝑡 for
the next time frame
(for the next
calculation in time)

6. STRUCK algorithm analysis
 The STRUCK approach is different from traditional
approaches by not using labels for the samples and by
predicting the next bounding box location using SVM
vector set
 The algorithm`s functionality is problematic due to several
reasons:
 Fast movements back and forth causes inaccuracy
 The object`s learning is not exclusive to the original object
but to the bounding box
 While the object exits the frame the algorithm loses it and
will recognize the first object to enter the bounding box

7. Examples for the STRUCK
functionality

8. TLD algorithm analysis
 The TLD algorithm has an amazing tracking and
learning ability, which characterized in the following
features:
 The algorithm succeeded tracking the object even for
frequent changes in the object`s distance and visibility
 While the object exits the frame the algorithm managed
to find it when it return to the framed
 The algorithm keeps an extensive database for the
visibility of the object that helps it to track the object
even when it is out of the frame

9. Examples for the TLD
functionality

10. Future work
 Studying the TLD more, so we can understand better
the differences between the 2 algorithms
 Understanding the C code for both of the algorithms
 Studying the BeagleBoard© platform

11. The BeagleBoard© platform
 The BeagleBoard© has several elements with real time
programing abilities to simulate embedded applications
 The platform have different analog and digital I/O