TRACKING OF PARTIALLY OCCLUDED OBJECTS IN VIDEO SEQUENCES
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TRACKING OF PARTIALLY OCCLUDED OBJECTS IN VIDEO SEQUENCES- MATLAB R2013
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- 1. Tracking of Partially Occluded Object in Video Sequences
PRAVEEN PALLAV (1DS09IS061) Under the Guidance of Mr. M.T Gopala
Krishna Associate. Professor, Dept. of ISE Department of
Information Science and Engineering Dayananda Sagar College of
Engineering
- 2. INTRODUCTION Occlusion:- Occlusions occur when the view of a
moving object is blocked completely or partially by other objects.
TYPES OF OCCLUSION Tracking of Partially Occluded Object in Video
Sequences
- 3. Object tracking in computer vision refers to the task of
tracking individual moving objects accurately from one frame to
another in an image sequence. Problem Statement The problem faced
in tracking can be broadly classified into the following:- Varying
illumination conditions. Partial occlusion of the objects.
Variation in the shape of target. Objective Creating a database for
experimental purpose in different environment indoor and outdoor.
Designing a robust object tracking mechanism in video sequences.
The proposed system is simulated in MATLAB R2013. This proposed
system is experimented on standard database available and our own
database. Tracking of Partially Occluded Object in Video
Sequences
- 4. Literature Survey Patches-based Markov random field model
for multiple object tracking under occlusion by Mingjun Wu ,
Xianrong Peng and Qiheng Zhang in 2010. They have proposed a new
method to this problem, building on the patch representation of
object appearance. They formulated multiple objects tracking as
classification tasks which competitively use the appearance models
of the interacting objects. Tracking of Multiple Objects under
Partial Occlusion by Bing Han, Christopher Paulson, Taoran Lu,
Dapeng Wu, Jian Li in 2004. They proposed an algorithm for
multi-object tracking under occlusion based on a new weighted
Kanade-Lucasi-Tomasi (KLT) tracker. Tracking of Partially Occluded
Object in Video Sequences
- 5. PROPOSED ALGORITHM STEP 1 Read the video sequence from the
dataset. STEP 2 Convert video sequences into a set of frames. STEP
3 Reserve first twenty frames for background registration STEP 4
Calculate the difference value using Df = abs( BG IM ) Where, BG =
Background image IM = Input frame And update the difference value
using Df = max(Df ,[ ] ,3) Tracking of Partially Occluded Object in
Video Sequences
- 6. STEP 5 Create the Binary Mask and apply morphological
operation using the following function STEP 6 Based on the binary
mask obtained, denoised mask is calculated and plotted. STEP 7
Using Lucas Kanade Feature Tracker algorithm, labeling of region of
interest with different color components for different objects.
STEP 8 If no track point is found in object then create new entry
in database and obtain the coordinates and initialize the
dictionary. STEP 9 Tracking is done based on the entries in the
dictionary and proper output is shown in case of occlusion.
Tracking of Partially Occluded Object in Video Sequences BW =
bwmorph( BW,'bridge')
- 7. FLOWCHART Tracking of Partially Occluded Object in Video
Sequences Initializing Dictionary
- 8. o The efficiency of the algorithm is verified by considering
standard database and our own database available. o The technique
that are used for detection and tracking is Kanade-Lucas- Tomasi
(KLT) tracker. o The proposed method is used for multi-object
tracking under occlusion by combining multiple cues(Color, Motion,
Features ). RESULTS AND DISCUSSION Tracking of Partially Occluded
Object in Video Sequences
- 9. ORIGINAL IMAGE OCCLUDED IMAGE DIFFERENCE VALUES WITH RESPECT
TO BACKGROUND Tracking of Partially Occluded Object in Video
Sequences
- 10. DENOISED IMAGE DENOISED IMAGE Tracking of Partially
Occluded Object in Video Sequences TRACKED IMAGE
- 11. RESULT ANALYSIS Tracking of Partially Occluded Object in
Video Sequences TRACKING BASED ON EXISTING SYSTEM TRACKING BASED ON
PROPOSED SYSTEM
- 12. APPLICATIONS Tracking of Partially Occluded Object in Video
Sequences Humancomputer Interaction (HCI) involves the study,
planning, and design of the interaction between people (users) and
computers. Anomaly detection, also referred to as outlier detection
refers to detecting patterns in a given data set that do not
conform to an established normal behavior. The patterns thus
detected are called anomalies. This project can be used as a
real-time traffic surveillance system for the detection,
recognition, and tracking of multiple vehicles in roadway images.
To count the number of objects in a video, multiple object tracking
can be used.
- 13. APPLICATIONS (Contd) Tracking of Partially Occluded Object
in Video Sequences In robot navigation, the steering system needs
to identify different obstacles in the path to avoid collision. If
the obstacles themselves are other moving objects then it calls for
a real-time object tracking system.
- 14. CONCLUSION The proposed method is used for multi-object
tracking under occlusion by combining multiple cues. Different
color patch for different object. Takes care of partially occluded
images. Dictionary is initialized when new object is detected.
Automated object detection and tracking. Tracking of Partially
Occluded Object in Video Sequences
- 15. Tracking of Partially Occluded Object in Video
Sequences