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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TRACKING OF PARTIALLY OCCLUDED OBJECTS IN VIDEO SEQUENCES

  1. 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. 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. 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. 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. 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. 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. 7. FLOWCHART Tracking of Partially Occluded Object in Video Sequences Initializing Dictionary
  8. 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. 9. ORIGINAL IMAGE OCCLUDED IMAGE DIFFERENCE VALUES WITH RESPECT TO BACKGROUND Tracking of Partially Occluded Object in Video Sequences
  10. 10. DENOISED IMAGE DENOISED IMAGE Tracking of Partially Occluded Object in Video Sequences TRACKED IMAGE
  11. 11. RESULT ANALYSIS Tracking of Partially Occluded Object in Video Sequences TRACKING BASED ON EXISTING SYSTEM TRACKING BASED ON PROPOSED SYSTEM
  12. 12. APPLICATIONS Tracking of Partially Occluded Object in Video Sequences  Human–computer 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. 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. 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. 15. Tracking of Partially Occluded Object in Video Sequences

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