In computer vision related applications, video analysis of human walking motion is currently one of the most active research topics. The task of analyzing human walking can be divided into three distinct subtaskshuman detection or segmentation, motion tracking and walking pose analysis. Typically, the analysis of the human walking starts with the extraction of motion information, detection of the presence of humans in the sequences of frames and then followed by analysis of events related to walking. This paper presents a survey of different methodologies used for human walking motion analysis, approaches used for human detection or segmentation, various tracking methods, approaches for pose estimation and pose analysis. The common data sets available for building robust, automatic and intelligent systems to understand "walking" motion are also included. Finally, uses of unsupervised techniques for analysing human walking are highlighted. Human walking motion is a subset of a broad topic of human motion analysis.

The consolidation of the virtual machines (VMs) helps to optimise the usage of resources and hence reduces the energy consumption in a cloud data centre. VM placement plays an important part in the consolidation of the VMs. The researchers have developed various algorithms for VM placement considering the optimised energy consumption. However, these algorithms lack the use of exploitation mechanism efficiently. This paper addresses VM placement issues by proposing two meta-heuristic algorithms namely, the enhanced modified firefly algorithm (MFF) and the hierarchical cluster based modified firefly algorithm (HCMFF), presenting the comparative analysis relating to energy optimisation. The comparisons are made against the existing honeybee (HB) algorithm, honeybee cluster based technique (HCT) and the energy consumption results of all the participating algorithms confirm that the proposed HCMFF is more efficient than the other algorithms. The simulation study shows that HCMFF consumes 12% less energy than honeybee algorithm, 6% less than HCT algorithm and 2% less than original firefly. The usage of the appropriate algorithm can help in efficient usage of energy in cloud computing.

Managing the transactions in real time distributed computing system is not easy, as it has

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In order to enhance resource utilization and power efficiency in cloud data centers it is important to perform Virtual Machine (VM) placement in an optimal manner. VM placement uses the method of mapping virtual machines to physical machines (PM). Cloud computing researchers have recently introduced various metaheuristic algorithms for VM placement considering the optimized energy consumption. However, these algorithms do not meet the optimal energy consumption requirements. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to address the issues with VM placement focusing on the energy consumption.. . The performance of the proposed algorithm is evaluated using three different workloads in CloudSim tool.. The evaluation process includes comparison of the proposed algorithm against the existing Genetic Algorithm (GA,) Optimized Firefly Search Algorithm (OFS), and Ant Colony (AC) algorithm. The comparison results illustrate that the proposed ECS algorithm consumes less energy than the participant algorithms while maintaining a steady performance for SLA and VM migration. The ECS algorithm consumes around 25% less energy than GA, 27% less than OFS, and 26% less than AC.