A Novel Image Retrieval Based on a Combination of Local and Global Histograms of Visual Words

Content-based image retrieval (CBIR) provides a sustainable solution to retrieve similar images from an image archive. In the last few years, the Bag-of-Visual-Words (BoVW) model gained attention and significantly improved the performance of image retrieval. In the standard BoVW model, an image is represented as an orderless global histogram of visual words by ignoring the spatial layout. The spatial layout of an image carries significant information that can enhance the performance of CBIR. In this paper, we are presenting a novel image representation that is based on a combination of local and global histograms of visual words. The global histogram of visual words is constructed over the whole image, while the local histogram of visual words is constructed over the local rectangular region of the image. The local histogram contains the spatial information about the salient objects. Extensive experiments and comparisons conducted on Corel-A, Caltech-256, and Ground Truth image datasets demonstrate that the proposed image representation increases the performance of image retrieval

A spatiotemporal analysis of the impact of lockdown and coronavirus on London’s bicycle hire scheme: from response to recovery to a new normal

The coronavirus pandemic that started in 2019 has had wide-ranging impacts on many aspects of people’s daily lives. At the peak of the outbreak, lockdown measures and social distancing changed the ways in which cities function. In particular, they had profound impacts on urban transportation systems, with public transport being shut down in many cities. Bike share systems (BSS) were widely reported as having experienced an increase in demand during the early stages of the pandemic before returning to pre-pandemic levels. However, the studies published to date focus mainly on the first year of the pandemic, when various waves saw continual relaxing and reintroductions of restrictions. Therefore, they fall short of exploring the role of BSS as we move to the post-pandemic period. To address this gap, this study uses origin-destination (O-D) flow data from London’s Santander Cycle Hire Scheme from 2019–2021 to analyze the changing use of BSS throughout the first two years of the pandemic, from lockdown to recovery. A Gaussian mixture model (GMM) is used to cluster 2019 BSS trips into three distinct clusters based on their duration and distance. The clusters are used as a reference from which to measure spatial and temporal change in 2020 and 2021. In agreement with previous research, BSS usage was found to have declined by nearly 30% during the first lockdown. Usage then saw a sharp increase as restrictions were lifted, characterized by longer, less direct trips throughout the afternoon rather than typical peak commuting trips. Although the aggregate number of BSS trips appeared to return to normal by October 2020, this was against the backdrop of continuing restrictions on international travel and work from home orders. The period between July and December 2021 was the first period that all government restrictions were lifted. During this time, BSS trips reached higher levels than in 2019. Spatio-temporal analysis indicates a shift away from the traditional morning and evening peak to a more diffuse pattern of working hours. The results indicate that the pandemic may have had sustained impacts on travel behavior, leading to a “new normal” that reflects different ways of working

Learning with Contexts

Ph.DDOCTOR OF PHILOSOPH

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Studies on Personalized HCI

Ph.DDOCTOR OF PHILOSOPH

Single to multiple target, multiple type visual tracking

Visual tracking is a key task in applications such as intelligent surveillance, humancomputer interaction (HCI), human-robot interaction (HRI), augmented reality (AR), driver assistance systems, and medical applications. In this thesis, we make three main novel contributions for target tracking in video sequences. First, we develop a long-term model-free single target tracking by learning discriminative correlation filters and an online classifier that can track a target of interest in both sparse and crowded scenes. In this case, we learn two different correlation filters, translation and scale correlation filters, using different visual features. We also include a re-detection module that can re-initialize the tracker in case of tracking failures due to long-term occlusions. Second, a multiple target, multiple type filtering algorithm is developed using Random Finite Set (RFS) theory. In particular, we extend the standard Probability Hypothesis Density (PHD) filter for multiple type of targets, each with distinct detection properties, to develop multiple target, multiple type filtering, N-type PHD filter, where N ≥ 2, for handling confusions that can occur among target types at the measurements level. This method takes into account not only background false positives (clutter), but also confusions between target detections, which are in general different in character from background clutter. Then, under the assumptions of Gaussianity and linearity, we extend Gaussian mixture (GM) implementation of the standard PHD filter for the proposed N-type PHD filter termed as N-type GM-PHD filter. Third, we apply this N-type GM-PHD filter to real video sequences by integrating object detectors’ information into this filter for two scenarios. In the first scenario, a tri-GM-PHD filter is applied to real video sequences containing three types of multiple targets in the same scene, two football teams and a referee, using separate but confused detections. In the second scenario, we use a dual GM-PHD filter for tracking pedestrians and vehicles in the same scene handling their detectors’ confusions. For both cases, Munkres’s variant of the Hungarian assignment algorithm is used to associate tracked target identities between frames. We make extensive evaluations of these developed algorithms and find out that our methods outperform their corresponding state-of-the-art approaches by a large margin.EPSR