Comparative Analysis of Temporal Segmentation Methods of Video Sequences

Comparative Analysis of Temporal Segmentation Methods of Video Sequences

Marcelo Saval-Calvo (University of Alicante, Spain), Jorge Azorín-López (University of Alicante, Spain) and Andrés Fuster-Guilló (University of Alicante, Spain)
DOI: 10.4018/978-1-4666-2672-0.ch003
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Abstract

In this chapter, a comparative analysis of basic segmentation methods of video sequences and their combinations is carried out. Analysis of different algorithms is based on the efficiency (true positive and false positive rates) and temporal cost to provide regions in the scene. These are two of the most important requirements of the design to provide to the tracking with segmentation in an efficient and timely manner constrained to the application. Specifically, methods using temporal information as Background Subtraction, Temporal Differencing, Optical Flow, and the four combinations of them have been analyzed. Experimentation has been done using image sequences of CAVIAR project database. Efficiency results show that Background Subtraction achieves the best individual result whereas the combination of the three basic methods is the best result in general. However, combinations with Optical Flow should be considered depending of application, because its temporal cost is too high with respect to efficiency provided to the combination.
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Background

Specifically among the works directly related to this chapter, analysis of efficiency of segmentation methods has been carried out recently. A comparative analysis of BG methods and its variations are frequent in the literature. It is worth mentioning the works of El Baf et al. (El Baf, 2007) and Hall et al. (Hall, 2005) in which a comparison of BG Simple Gaussian, Mixture of Gaussians, Kernel Density Estimation, W4 (Haritaoglu, 2000) and LOTS method (based on different background models) can be found. Also, Benezeth et al. (Benezeth, 2010) perform a wide comparison with an extensive database of sequences and methods based on the BG. Interesting analysis of contour based methods has been carried out by VenuGopal et al. (VenuGolap, 2011) and Arbelaez et al. (Arbelaez, 2009). These works compare segmentation based on border extraction methods including Canny, Sobel and Laplacian of Gaussians. A comparison of parametric and non-parametric methods was proposed by Herrero et al. (Herrero, 2009). Basic methods (Temporal differencing, Median filter), parametric (Simple, Mixture of Gaussians and Gamma algorithm), and non-parametric methods (Histogram-based approach, Kernel Density Estimation) were analyzed concluding that parametric methods have the best results but they have problems to properly adjust the parameters. Finally, it is interesting to mention the comparative of region and contour based methods proposed by Zhang (Zhang, 1997).

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