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An Overview of Block Matching Algorithms for Motion Vector Estimation

Khawase Sonam T., Kamble Shailesh D, Thakur Nileshsingh V., Patharkar Akshay S.

Annals of Computer Science and Information Systems

2017

Vol. 10

217--222

In video compression technique, motion estimation is one of the key components because of its high computation complexity involves in finding the motion vectors (MV) between the frames. The purpose of motion estimation is to reduce the storage space, bandwidth and transmission cost for transmission of video in many multimedia service applications by reducing the temporal redundancies while maintaining a good quality of the video. There are many motion estimation algorithms, but there is a trade-off between algorithms accuracy and speed. Among all of these, block-based motion estimation algorithms are most robust and versatile. In motion estimation, a variety of fast block based matching algorithms has been proposed to address the issues such as reducing the number of search/checkpoints, computational cost, and complexities etc. Due to its simplicity, the block-based technique is most popular. Motion estimation is only known for video coding process but for solving real life applications many researchers from the different domain are attracted towards block matching algorithms for motion vector estimation.This paper is a review of various block matching algorithms based on shapes and patterns as well as block matching criteria used for motion estimation.

Optical feature clustering algorithm for object tracking in image sequences

Pełczyński P., Arriola J. A.

Image Processing & Communications

2009

Vol. 14, no 2-3

23-30

The aim of the presented work was the development of software technique for detection and tracking of moving objects in video sequences. It is intended to serve as an automatic video surveillance or traffic control system. Local image features are detected and tracked in the presented system. Two clustering algorithms are utilised for this task succes-fully. Firstly, the QT (Quality Threshold) algorithm has a potential of new object detection. Secondly, modification of a well known K-means algorithm proved its usefulness in tracking moving objects in image sequences. For reduction of the analysed data, corners are detected in consecutive images. Their motion vector and coordinates produce feature vectors for an image classifier. The obtained results show the ability of the proposed technique to detect and track multiple objects on the basis of their local, visual features. No model matching technique was necessary, which simplified overall approach. Comparatively low number of operations, required to perform tracking process, gives the possibility to implement the algorithm in real time on modern graphics processing unit in PC computers.