Quantized Kalman Filter-Based Pattern Matching for Detection and Tracking of Moving Objects
Matheswari Rajamanickam

Matheswari Rajamanickam, Assistant Professor, Department of Computer Science, M.E.S College of Arts, Commerce and Science, Bangalore, Karnataka, India.

Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 3842-3851 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9839109119/2019©BEIESP | DOI: 10.35940/ijeat.A9839.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Detection And Tracking of Multiple Moving Objects From A Sequence of Video Frame And Obtaining Visual Records of Objects Play An Important Role In The Video Surveillance Systems. Transform And Filtering Technique Designed For Video Pattern Matching And Moving Object Detection, Failed To Handle Large Number of Objects In Video Frame And Further Needs To Be Optimized. Several Existing Methods Perform Detection And Tracking of Moving Objects. However, The Performance Efficiency of The Existing Methods Needs To Be Optimized To Achieve More Robust And Reliable Detection And Tracking of Moving Objects. In order To Improve The Pattern Matching Accuracy, A Quantized Kalman Filter-Based Pattern Matching (Qkf-Pm) Technique Is Proposed For Detecting And Tracking of Moving Objects. The Present Phase Includes Three Functionalities: Top-Down Approach, Kernel Pattern Segment Function And Kalman Filtering. First, The Top-Down Approach Based On Kalman Filtering (Kf) Technique Is Performed To Detect The Chromatic Shadows of Objects. Next, Kernel Pattern Segment Function Creates The Seed Points For Detecting Moving object Pattern. Finally, object Tracking Is Performed Using The Proposed Quantized Kalman Filter Based on The Center of Seed Point Affinity Feature Values Are Used To Track The Moving Objects In A Particular Region Using The Minimum Bounding Box Approach. Experimental Results Reveals That The Proposed Qkf-Pm Technique Achieves Better Performance In Terms of True Detection Rate, Pattern Matching Accuracy, Pattern Matching Time, And object Tracking Accuracy With Respect To The Number of Video Frames Per Second.
Keywords: Moving object detection, Tracking, Quantized Kalman Filter, Pattern Matching, Top-down approach, kernel pattern, Seed point, Bounding box.