Object tracking presentation

Computer vision has received great attention over the last two decades.
 This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.

1. A Presentation
on
Object Tracking
Presented by
ShwetaKanhere-Banait
[email protected]

2. Outline
•Introduction to Object tracking
•Definition
•Flowchart
•Tracking classifications
•Object Tracking Algorithms
•Comparison
•Challenges of object tracking
•Observations/Conclusion
•Application
•Future work
•References
•Thank you

3. Introduction to Object tracking
•Computer vision has received great attention over the last two decades.
•This research field is important not only in security related software, but also
in advanced interface between people and computers, advanced control
methods and many other areas.
•Object tracking play a key role .

4. Definition
Object tracking is a discipline within computer vision, which aims to track
objects as they move across a series of video frames. Objects are often people,
but may also be animals, vehicles or other objects of interest, such as the ball in
a game of soccer.

5. Flowchart

6. Tracking classifications
•Primitive geometric shapes
•Articulated shape models
•Skeletal models
•Point Tracking
•Kernel Tracking
•Silhouette Tracking

7. Point Tracking
Deterministic
Method
Maximum
velocity
Smooth motion
Proximal uniformity
Common motion
Proximity
Rigidity
Statistical or Probabilistic Method
Multiple object Tracking.
Single object
Tracking.
Kalman Filter
Particle Filter
Joint Probability Data
Association Filter (JPDAF)
Multiple Hypothesis Tracking

8. Kernel Tracking
Support Vector Machines
(SVM)
Template Tracking
Mean Shift
Method
Layering Based-Matching

9. Silhouette-Based Object Tracking
•When object is represented by the outlines with only single solid color
in between the outlines made up of edges is known as silhouette.
•It is used where object cannot be represented by the simple geometric
shapes or by set of points.
•Silhouette is feature-less, therefore object model is created with help
of contour, edge information, color histogram etc.

10. Object Tracking Algorithms
1. Absolute Differences 2. Census Method 3. Feature Based Method
•Mean-shift
•KLTP
•Condensation
•Tracking-Learning-Detection (TLD)
•Tracking Based on Boundary of the Object
Popular object tracking algorithms that use deep learning methods:
•SORT
•GOTURN
•MDNet

11. SORT
•Object detection Engine
•The algorithm tracks multiple objects in real time, associating the objects
in each frame with those detected in previous frames using simple
heuristics

12. …SORT

13. …SORT
Poor
Good
Excellent

14. …SORT

15. GOTURN
•Generic Object Tracking Using Regression Network
•GOTURN is trained by comparing pairs of cropped frames from
thousands of video sequences

16. …GOTURN

17. …GOTURN
•https://youtu.be/kMhwXnLgT_I

18. ….MDNet
•Multi Domain Network (MDNet) is a CNN architecture that won the VOT2015
challenge.
•The objective of MDNetis to speed up training in order to provide real-time results
•https://youtu.be/zYM7G5qd090

21. Challenges of object tracking
•Re-identification
•Appearance and disappearance
•Occlusion (snow ,storm, snow on the ground, fog, air turbulence etc)
•Illumination
•Co-ordinates matching in case of multiple camera systems
•Pose variation of the object
•Motion blur
To perform tracking with these challenges in real time make tracking tedious

22. Observations
•Tracking approaches that employ a stable model can only accommodate
small changes in the object appearance but do not explicitly handle
severe occlusions or continuous appearance changes.
•A potential approach to overcome the limitation is to learn different
views of the object and later use them during tracking.
•A tracker that takes advantage of contextual information to incorporate
general constraints on shape and motion of objects will usually perform
better than the one that does not exploit this information.
•The capability to learn object models online may greatly increase the
applicability of a tracker

23. Application
•Video surveillance,
•Vision-based control
•Video compression
•Human computer interfaces
•Robotics

24. Future work
•Theaccuracyofobjecttrackingcouldpotentiallyincreasebydeveloping
methodsforamoreautomaticselectionprocessoffeatures.
•Weknowfromexperiencethatahumantendsdomakemoremistakethana
computerprogramoptimizedforacertainpurpose.
•Automaticfeatureselectionhasreceivedattentionintheareaofpattern
recognition,wheremethodsforthispurposearedividedintofiltermethods
andwrappermethods.
•However,thesehavenotgottenthesameattentionintheareaofobject
tracking,wherefeatureselectionstillismostlydonemanually.
•Therecouldberoomforimprovementinobjecttrackingbydevelopingfast
andaccuratemethodsforautomaticfeatureselection.

25. References:
•Z. Kalal, K. Mikolajczyk, and J. Matas, “Tracking-Learning-Detection,” Pattern Analysis and Machine Intelligence, 2011.
•Moving Object Detection Approaches, Challenges and Object Tracking
•https://missinglink.ai/guides/computer-vision/object-tracking-deep-learning/
•Li, F. Li, and M. Zhang, “A Real-time Detecting and Tracking Method for Moving Objects BasedonColor Video,” in 2009 Sixth
International Conference on Computer Graphics, Imaging andVisualization. IEEE, 2009, pp. 317–322.
•Object Detection and Tracking,FatihPorikliand AlperYilmaz
•Abdurrahman, "Smart video-based surveillance: Opportunities and challenges from image processing perspectives," 2016 3
rd
International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Semarang, 2016, pp.
10-10
•W. Kim and C. Jung, "Illumination-Invariant Background Subtraction: Comparative Review, Models, and Prospects," in IEEE
Access, vol. 5, pp. 8369-8384, 2017.
•AseemaMohantyand SanjivaniShantaiya. Article: A Survey on Moving Object Detection using Background Subtraction Methods
in Video. IJCA Proceedings on National Conference on Knowledge , Innovation in Technology and Engineering
(NCKITE 2015)NCKITE, 2015(2):5-10, July 2015
•M. Zhu and H. Wang, "Fast detection of moving object based on improved frame-difference method," 2017 6th International
Conference on Computer Science and Network Technology (ICCSNT), Dalian, 2017, pp. 299-303.
•NesneTakibindeUyarlanabilirAramaAlanı Adaptive Search Area in the Object Tracking , KazımHANBAY , Bingöl
Üniversitesi, BilgisayarMühendisliğiBölümü, Bingöl, Türkiye, Bingöl, Türkiye

26. Thank you!

27. Discussion !