Mobile Location Indexing Based On Synthetic Moving Objects

Today, the number of researches based on the data they move known as mobile objects indexing came out from the traditional static one. There are some indexing approaches to handle the complicated moving positions. One of the suitable ideas is pre-ordering these objects before building index structure. In this paper, a structure, a presorted-nearest index tree algorithm is proposed that allowed maintaining, updating, and range querying mobile objects within the desired period. Besides, it gives the advantage of an index structure to easy data access and fast query along with the retrieving nearest locations from a location point in the index structure. A synthetic mobile position dataset is also proposed for performance evaluation so that it is free from location privacy and confidentiality. The detail experimental results are discussed together with the performance evaluation of KDtree-based index structure. Both approaches are similarly efficient in range searching. However, the proposed approach is especially much more save time for the nearest neighbor search within a range than KD tree-based calculation

Partial 3D Object Retrieval using Local Binary QUICCI Descriptors and Dissimilarity Tree Indexing

A complete pipeline is presented for accurate and efficient partial 3D object retrieval based on Quick Intersection Count Change Image (QUICCI) binary local descriptors and a novel indexing tree. It is shown how a modification to the QUICCI query descriptor makes it ideal for partial retrieval. An indexing structure called Dissimilarity Tree is proposed which can significantly accelerate searching the large space of local descriptors; this is applicable to QUICCI and other binary descriptors. The index exploits the distribution of bits within descriptors for efficient retrieval. The retrieval pipeline is tested on the artificial part of SHREC'16 dataset with near-ideal retrieval results.Comment: 19 pages, 17 figures, to be published in Computers & Graphic

Energy Efficient Rectangular Indexing for Mobile Peer-to-Peer Environment

Now a days in wireless environment there are many challenges. One of them which is need to be addressed in mobile Peer-to-Peer environment is getting the information of interest quickly and efficiently. Wherein whenever the node tries to get the desired data it has to wait too long or have to contact to unnecessary nodes which are not having their data of interest. This causes the node to waste the limited power resources and incurs more cost in terms of energy wastage. Here we proposed an energy efficient rectangular indexing called PMBR (Peer-to-Peer Minimum Bounding Rectangle) which allows the user to get the information of interest in energy efficient manner. We proposed algorithms namely PMBR_DSS, PMBR_HB and PMBR_CP and processed Nearest Neighbor & Range type queries. The experimental results carried out shows that the proposed algorithm PMBR_CP provides the efficient, quick and assured access to information of interest by saving the scarce power resources

Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

Experience based action planning for environmental manipulation in autonomous robotic systems

The ability for autonomous robots to plan action sequences in order to manipulate their environment to achieve a specific goal is of vital importance for agents which are deployed in a vast number of situations. From domestic care robots to autonomous swarms of search and rescue robots there is a need for agents to be able to study, reason about, and manipulate their environment without the oversight of human operators. As these robots are typically deployed in areas inhabited and organised by humans it is likely that they will encounter similar objects when going about their duties, and in many cases the objects encountered are likely to be arranged in similar ways relative to one another. Manipulation of the environment is an incredibly complex task requiring vast amounts of computation to generate a suitable state of actions for even the simplest of tasks. To this end we explore the application of memory based systems to environment manipulation planning. We propose new search techniques targeted at the problem of environmental manipulation for search and rescue, and recall techniques aimed at allowing more complex planning to take place with lower computational cost. We explore these ideas from the perspective of autonomous robotic systems deployed for search and rescue, however the techniques presented would be equally valid for robots in other areas, or for virtual agents interacting with cyber-physical systems