Current Issue : January - March Volume : 2012 Issue Number : 1 Articles : 5 Articles
We propose a learning-based background subtraction approach based on the theory of sparse representation and dictionary learning. Our method makes the following two important assumptions: (1) the background of a scene has a sparse linear representation over a learned dictionary; (2) the foreground is ââ?¬Å?sparseââ?¬Â in the sense that majority pixels of the frame belong to the background. These two assumptions enable our method to handle both sudden and gradual background changes better than existing methods. As discussed in the paper, the way of learning the dictionary is critical to the success of background modeling in our method. To build a correct background model when training samples are not foreground-free, we propose a novel robust dictionary learning algorithm. It automatically prunes foreground pixels out as outliers at the learning stage. Experiments in both qualitative and quantitative comparisons with competing methods demonstrate the obtained robustness against background changes and better performance in foreground segmentation....
This paper proposes an approach for tracking multiple articulated targets using a combined data association and evolving population particle filter. A visual target is represented as a pictorial structure using a collection of parts together with a model of their geometry. Tracking multiple targets in video involves an iterative alternating scheme of selecting valid measurements belonging to a target from a clutter or other measurements that all fall within a validation gate. An algorithm with extended likelihood probabilistic data association and evolving groups of populations of particles representing a multiple-part distribution is designed. Variety in the particles is introduced using constrained genetic operators both in the sampling and resampling steps. We explore the effect of various model parameters on system performance and show that the proposed model achieves better accuracy than other widely used methods on standard datasets....
Subjective testing is the most direct means of assessing multimedia quality as experienced by users. When multiple dimensions must be evaluated, these tests can become slow and costly. We present gradient ascent subjective testing (GAST) as an efficient way to locate optimizing sets of coding or transmission parameter values. GAST combines gradient ascent optimization techniques with subjective test trials. As a proof-of-concept, we used GAST to search a two-dimensional parameter space for the known region of maximal audio quality, using paired-comparison listening trials. That region was located accurately and much more efficiently than use of an exhaustive search. We also used GAST to search a two-dimensional quantizer design space for a point of maximal image quality, using side-by-side paired-comparison trials. The point of maximal image quality was efficiently located, and the corresponding quantizer shape and deadzone agree closely with the quantizer specifications for JPEG 2000, Part 1....
Detecting static objects in video sequences has a high relevance in many surveillance applications, such as the detection of abandoned objects in public areas. In this paper, we present a system for the detection of static objects in crowded scenes. Based on the detection of two background models learning at different rates, pixels are classified with the help of a finite-state machine. The background is modelled by two mixtures of Gaussians with identical parameters except for the learning rate. The state machine provides the meaning for the interpretation of the results obtained from background subtraction; it can be implemented as a look-up table with negligible computational cost and it can be easily extended. Due to the definition of the states in the state machine, the system can be used either full automatically or interactively, making it extremely suitable for real-life surveillance applications. The system was successfully validated with several public datasets....
Research in the field of video quality assessment relies on the availability of subjective scores, collected by means of experiments in which groups of people are asked to rate the quality of video sequences. The availability of subjective scores is fundamental to enable validation and comparative benchmarking of the objective algorithms that try to predict human perception of video quality by automatically analyzing the video sequences, in a way to support reproducible and reliable research results. In this paper, a publicly available database of subjective quality scores and corrupted video sequences is described. The scores refer to 156 sequences at CIF and 4CIF spatial resolutions, encoded with H.264/AVC and corrupted by simulating the transmission over an error-prone network. The subjective evaluation has been performed by 40 subjects at the premises of two academic institutions, in standard-compliant controlled environments. In order to support reproducible research in the field of full-reference, reduced-reference, and no-reference video quality assessment algorithms, both the uncompressed files and the H.264/AVC bitstreams, as well as the packet loss patterns, have been made available to the research community....
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