Estimation of Moving Information for Tracking of Moving Objects

• Published in: Journal of mechanical science and technology 2001-03, Vol.15 (3), p.300-308
• Main Authors: KANG, Sung-Kwan, PARK, Jong-An, JEONG, Sang-Hwa
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Cognition & reasoning; Combinatorial analysis; Computation; Computer science; control theory; systems; Exact sciences and technology; Gender differences; Image processing; Information, signal and communications theory; Mathematical analysis; Pattern recognition. Digital image processing. Computational geometry; Real time; Signal processing; Streams; Telecommunications and information theory; Tracking; Vectors (mathematics); Walking
• ISSN: 1226-4865; 1738-494X; 1976-3824
• DOI: 10.1007/BF03185213

Tracking of moving objects within video streams is a complex and time-consuming process. Large number of moving objects increases the time for computation of tracking the moving objects. Because of large computations, there are real-time processing problems in tracking of moving objects. Also, the change of environment causes errors in estimation of tracking information. In this paper, we present a new method for tracking of moving objects using optical flow motion analysis. Optical flow represents an important family of visual information processing techniques in computer vision. Segmenting an optical flow field into coherent motion groups and estimating each underlying motion are very challenging tasks when the optical flow field is projected from a scene of several moving objects independently. The problem is further complicated if the optical flow data are noisy and partially incorrect. Optical flow estimation based on regulation method is an iterative method, which is very sensitive to the noisy data. So we used the Combinatorial Hough Transform (CHT) and Voting Accumulation for finding the optimal constraint lines. To decrease the operation time, we used logical operations. Optical flow vectors of moving objects are extracted, and the moving information of objects is computed from the extracted optical flow vectors. The simulation results on the noisy test images show that the proposed method finds better flow vectors and more correctly estimates the moving information of objects in the real time video streams.[PUBLICATION ABSTRACT]