M. Manzur Murshed

Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic

In this paper, we propose the AVVMC VM consolidation scheme that focuses on balanced resource utilization of servers across different computing resources (CPU, memory, and network I/O) with the goal of minimizing power consumption and resource wastage. Since the VM consolidation problem is strictly NP-hard and computationally infeasible for large data centers, we propose adaptation and integration of the Ant Colony Optimization (ACO) metaheuristic with balanced usage of computing resources based on vector algebra. Our simulation results show that AVVMC outperforms existing methods and achieves improvement in both energy consumption and resource wastage reduction.

Improved Gaussian mixtures for robust object detection by adaptive multi-background generation

Adaptive Gaussian mixtures are widely used to model the dynamic background for real-time object detection. Recently the convergence speed of this approach is improved and a relatively robust statistical framework is proposed by Lee (PAMI, 2005). However, object quality still remains unacceptable due to poor Gaussian mixture quality, susceptibility to background/foreground data proportion, and inability to handle intrinsic background motion. This paper proposes an effective technique to eliminate these drawbacks by modifying the new model induction logic and using intensity difference thresholding to detect objects from one or more believe-to-be backgrounds. Experimental results on two benchmark datasets confirm that the object quality of the proposed technique is superior to that of Leepsilas technique at any model learning rate.

A real-time pattern selection algorithm for very low bit-rate video coding using relevance and similarity metrics

Very low bit-rate video coding using regularly shaped patterns to represent moving regions in macroblocks has good potential for improved coding efficiency. This paper presents a real-time pattern selection (RTPS) algorithm, which uses a pattern relevance and similarity metric to achieve faster pattern selection from a large codebook. For each applicable macroblock, the relevance metric is applied to create a customized pattern codebook (CPC) from which the best pattern is selected using the similarity metric. The CPC size is adapted to facilitate real-time selection. Results prove the quantitative and perceptual performance of RTPS is superior to both the Fixed-8 algorithm and H.263.

Video Coding Focusing on Block Partitioning and Occlusion

Among the existing block partitioning schemes, the pattern-based video coding (PVC) has already established its superiority at low bit-rate. Its innovative segmentation process with regular-shaped pattern templates is very fast as it avoids handling the exact shape of the moving objects. It also judiciously encodes the pattern-uncovered background segments capturing high level of interblock temporal redundancy without any motion compensation, which is favoured by the rate-distortion optimizer at low bit-rates. The existing PVC technique, however, uses a number of content-sensitive thresholds and thus setting them to any predefined values risks ignoring some of the macroblocks that would otherwise be encoded with patterns. Furthermore, occluded background can potentially degrade the performance of this technique. In this paper, a robust PVC scheme is proposed by removing all the content-sensitive thresholds, introducing a new similarity metric, considering multiple top-ranked patterns by the rate-distortion optimizer, and refining the Lagrangian multiplier of the H.264 standard for efficient embedding. A novel pattern-based residual encoding approach is also integrated to address the occlusion issue. Once embedded into the H.264 Baseline profile, the proposed PVC scheme improves the image quality perceptually significantly by at least 0.5 dB in low bit-rate video coding applications. A similar trend is observed for moderate to high bit-rate applications when the proposed scheme replaces the bi-directional predictive mode in the H.264 High profile.

An Affine Resilient Curvature Scale-Space Corner Detector

Curvature scale-space (CSS) corner detectors look for curvature maxima or inflection points on planar curves. They use arc-length parameterized curvature. Therefore, they are not robust to affine transformations since the arc-length of a curve is not preserved under affine transformations. However, the affine-length of a curve is relatively invariant to affine transformations. This paper presents an improved CSS corner detector by applying the affine-length parameterized curvature to the CSS corner detection technique. A thorough robustness study has been carried out on a large database considering a wide range of affine transformations.

An Efficient Transmission Scheme for Minimizing User Waiting Time in Video-On-Demand Systems

To take the advantage of skewed popularity of videos, efficient video-on-demand (VOD) systems are more likely to deliver the most popular videos through periodic broadcasting and the least popular videos through on-demand multicasting. While videos delivered through multicasting usually share a pool of server channels, broadcasting of each video demands one or more channels dedicated to it. Given a total number of available channels, distributing them for individual broadcasting and the multicasting pool to achieve the optimal average user waiting time is a nonlinear optimization problem. This letter addresses this problem by proposing a hybrid transmission scheme, which uses dynamic programming approach to ensure optimally for any given number of channels and request arrival rate

On Stable Dynamic Background Generation Technique Using Gaussian Mixture Models for Robust Object Detection

Gaussian mixture models (GMM) is used to represent the dynamic background in a surveillance video to detect the moving objects automatically. All the existing GMM based techniques inherently use the proportion by which a pixel is going to observe the background in any operating environment. In this paper we first show that such a proportion not only varies widely across different scenarios but also forbids using very fast learning rate. We then propose a dynamic background generation technique in conjunction with basic background subtraction which detected moving objects with improved stability and superior detection quality on a wide range of operating environments in two sets of benchmark surveillance sequences.

New mobility based call admission control with on-demand borrowing scheme for QOS provisioning

In this paper, an advanced call admission control strategy is proposed in which bandwidth is allocated more efficiently and effectively to neighbouring cells by exploiting key mobility parameters to provide consistent Quality of Service (QoS) guarantees for multimedia traffic. Concomitantly, to ensure continuity of on-going calls with better utilization of resources, bandwidth is borrowed from existing adaptive calls without affecting the minimum QoS guarantee. The performance of the scheme is compared with other techniques including the rate-based borrowing scheme and implicit QoS provisioning strategy. Simulation results prove that this new scheme offers significant improvements in the requisite performance metrics of call blocking probability, call dropping probability, and bandwidth utilization, under a variety of differing traffic conditions.

Search and tracking algorithms for swarms of robots

Target search and tracking is a classical but difficult problem in many research domains, including computer vision, wireless sensor networks and robotics. We review the seminal works that addressed this problem in the area of swarm robotics, which is the application of swarm intelligence principles to the control of multi-robot systems. Robustness, scalability and flexibility, as well as distributed sensing, make swarm robotic systems well suited for the problem of target search and tracking in real-world applications. We classify the works we review according to the variations and aspects of the search and tracking problems they addressed. As this is a particularly application-driven research area, the adopted taxonomy makes this review serve as a quick reference guide to our readers in identifying related works and approaches according to their problem at hand. By no means is this an exhaustive review, but an overview for researchers who are new to the swarm robotics field, to help them easily start off their research. Surveys algorithms applicable to swarm robotic systems for target search and tracking.Identifies variations of the search and tracking problem addressed in the literature.Discusses desired capabilities of search and tracking algorithms for robot swarms.

Perception-Inspired Background Subtraction

Developing universal and context-invariant methods is one of the hardest challenges in computer vision. Background subtraction (BS), an essential precursor in most machine vision applications used for foreground detection, is no exception. Due to overreliance on statistical observations, most BS techniques show unpredictable behavior in dynamic unconstrained scenarios in which the characteristics of the operating environment are either unknown or change drastically. To achieve superior foreground detection quality across unconstrained scenarios, we propose a new technique, called perception-inspired background subtraction (PBS), which avoids overreliance on statistical observations by making key modeling decisions based on the characteristics of human visual perception. PBS exploits the human perception-inspired confidence interval to associate an observed intensity value with another intensity value during both model learning and background-foreground classification. The concept of perception-inspired confidence interval is also used for identifying redundant samples, thus ensuring the optimal number of samples in the background model. Furthermore, PBS dynamically varies the model adaptation speed (learning rate) at pixel level based on observed scene dynamics to ensure faster adaptation of changed background regions, as well as longer retention of stationary foregrounds. Extensive experimental evaluations on a wide range of benchmark datasets validate the efficacy of PBS compared to the state of the art for unconstraint video analytics.