Motion Estimation In Video Sequences Research Articles

An improved block matching algorithm for motion estimation in video sequences and application in robotics

Block Matching is one of the most efficient techniques for motion estimation for video sequences. Metaheuristic algorithms have been used effectively for motion estimation. In this paper, we propose two hybrid algorithms: Artificial Bee Colony with Differential Evolution and Harmony Search with Differential Evolution based motion estimation algorithms. Extensive experiments are conducted using four standard video sequences. The video sequences utilized for experimentation have all essential features such as different formats, resolutions and number of frames which are generally required in input video sequences. We compare the performance of the proposed algorithms with other algorithms considering various parameters such as Structural Similarity, Peak Signal to Noise Ratio, Average Number of Search Points etc. The comparative results demonstrate that the proposed algorithms outperformed other algorithms.

Using quasi-continuous histograms for fuzzy main motion estimation in video sequence

This paper presents a new fuzzy framework for main motion estimation in video sequences. The estimation is performed using a fuzzy representation of pixel gray levels. The motion is characterized by a set of parameters such as horizontal translation, rotation, etc. The method is based on a Hough-like vote procedure. In this scheme, the parametric space is discretized and each pixel votes for each bin of this discrete space. The votes are accumulated in a “quasi-continuous histogram’’ (QCH). The use of possibility theory and imprecise probabilities provides an accurate estimation of the histogram's mode related to the main motion. The advantages of quasi-continuous histograms in terms of accuracy and robustness are discussed in this paper. Very promising results were obtained using real and simulated video sequences. Comparative studies with classical methods are also presented.

Automatic feature-based global motion estimation in video sequences

In this paper, automatic and efficient global motion estimation for video sequences is proposed. In this estimation, we find and pick up pairs of feature points in the edge lines within the successive frames using the match algorithm. We then choose the best set of three pair points to measure the parameters of the Affine Model which describes the global motion. The result shows that we can obtain the Affine parameters of global motion efficiently in video sequences.

Automatic Synthesis of Motion Estimation Processors Based on a New Class of Hardware Architectures

A new class of fully parameterizable multiple array architectures for motion estimation in video sequences based on the Full-Search Block-Matching algorithm is proposed in this paper. This class is based on a new and efficient AB2 single array architecture with minimum latency, maximum throughput and full utilization of the hardware resources. It provides the ability to configure the target processor within the boundary values imposed for the configuration parameters concerning the algorithm setup, the processing time and the circuit area. With this purpose, a software configuration tool has been implemented to determine the set of possible configurations which fulfill the requisites of a given video coder. Experimental results using both FPGA and ASIC technologies are presented. In particular, the implementation of a single array processor configuration on a single-chip is illustrated, evidencing the ability to estimate motion vectors in real-time.