A Novel Filtering Approach for Tracking Visual Objects
Manne Dinesh Kumar1, Krishna Samalla2

1Manne Dinesh Kumar, Department Electronics and Communication Engineering. MTech in Digital Systems and Computer Electronics from Sreenidhi Institute of Science and Technology. Ghtatkesar, (Telangana), India.
2Dr. Krishna Samalla, Professor, Department of Electronics and Communication Engineering in Sreenidhi Institute of Science and Technology. Ghtatkesar, (Telangana), India.

Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 1066-1069 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2659037619/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Visual object tracking of moving objects is a dynamic area of research in computer vision. In developing video surveillance systems, it requires fast, consistent and robust algorithms for poignant object detection, classification, tracking, and activity analysis. Explicitly, tracking of multiple objects is more complicated than single object tracking. This paper suggests an algorithm by using a constant acceleration Kalman filter to track visual objects of variant sizes such as cars, ball and humans by varying few factors. Gaussian Mixture Model (GMM) is used for object detection using background subtraction. A blob analysis is carried for calculating area and centroid of detected objects. Theses, parameters are used for predicting and updating the location of tracked object using a Kalman filter. The proposed Kalman filter uses a constant acceleration model, as it is capable of tracking objects in all possible conditions of occlusions. The occlusion problem is minimized by defining a suitable cost function. Experiments using MATLAB show that the simulated results of proposed algorithm are accurate and can be used for real time multiple visual object tracking.
Keywords: Blob Analysis, Cost Function, Gaussian Mixture Model (GMM), Kalman Filter, Visual Object Tracking
Scope of the Article: Expert Approaches