Efficient people counting with limited manual interferences

© 2014 IEEE. People counting is a topic with various practical applications. Over the last decade, two general approaches have been proposed to tackle this problem: a) counting based on individual human detection; b)counting by measuring regression relation between the crowd density and number of people. Because the regression based method can avoid explicit people detection which faces several well-known challenges, it has been considered as a robust method particularly on a complicated environments. An efficient regression based method is proposed in this paper, which can be well adopted into any existing video surveillance system. It adopts color based segmentation to extract foreground regions in images. Regression is established based on the foreground density and the number of people. This method is fast and can deal with lighting condition changes. Experiments on public datasets and one captured dataset have shown the effectiveness and robustness of the method

Advances in Object and Activity Detection in Remote Sensing Imagery

The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms

Master of Science

thesisThis thesis encompasses the experimentation and development of neutron activation analysis protocols for the University of Utah Nuclear Engineering Program (UNEP). The University of Utah TRIGA Reactor (UUTR) was used as a neutron source to activate various materials to examine the inorganic elements. The Activity Estimator calculator was developed to approximate the activities of activated isotopes. Gamma ray activities, from activated samples, were acquired and measured on high purity germanium gamma spectroscopy detectors. Using the data collected from the gamma spectroscopy activated isotopes were identified and quantified. The activities from the identified isotopes were used to calculate the elemental concentrations of the sample materials using the Elemental Concentration Calculator and SRM Ratio Calculator. Complete NAA protocols and procedures were developed for a wide variety of materials and uses such as: criminal forensics, metals in soil, rock and water as well as minerals in fruits and vegetables

MR-Compatible Blood Sampler for PET

Over the last few years, the idea of simultaneous MR-PET imaging has attracted more and more research interest. This new bimodal technique promises accurate structural and functional information of the investigated object at the same time. While PET-CT has already established as a powerful bimodal imaging technique, MR offers better distinction of soft matter, which can be advantageous especially for brain research. Future studies in this field could also include simultaneous scans with fMRI and PET. For certain measurements (e.g. cerebral blood flow, metabolic rates), the quantitative analysis of PET data requires knowledge of the arterial input function (time-activity-curve of patient blood). In other words, the amount of radioactivity in the arterial blood has to be monitored constantly. Ordinary, commercially available blood sampling systems are based on Photo Multiplier Tubes (PMTs) that cannot be operated in an MR environment. An MR-compatible blood sampler was therefore designed and built to be able to exploit the full potential of hybrid MR-PET. Basically, the new device works as follows. Arterial blood is drawn out of the patient and conducted via a catheter through the detector unit of the blood sampler. The two annihilation photons that emerge after positron decay are detected separately by two scintillation crystals (50mm x 40mm x 30mm Lutetium Oxyorthosilicate (LSO)) that surround the catheter in a sandwich-like geometry. Each scintillation crystal is coupled to a single Avalanche Photodiode (APD). Both signals are fed through a 12m cable to the MR filter plate, where they are low-pass filtered. The pulse processing electronics, which are set behind the filter plate, are essentially performing a coincidence detection of annihilation photons. A major technical challenge was to deal with the pronounced temperature sensitivity and the relatively noisy signals of APDs. Besides appropriate considerations for the mechanical and electronical design, the solution involved the development of a new online algorithm that monitors the effective gain of the APDs and corrects for gain drifts. The prototype system was successfully tested for MR-compatibility in a Siemens Magnetom Trio MR tomograph. Furthermore, the blood sampler was used during PET scans of rats to prove the applicability of the new device

Development of a database and its use in the Investigation of Interferences in SRM assay design

Selected Reaction Monitoring (SRM), is a form of mass spectrometry that guarantees high throughput and also a high level of selectivity and specificity. Performing SRM experiments requires the development of assays to aid in peptide identification. This is a time consuming and expensive process thus biological researchers have come up with bioinformatics solutions for the design of SRM assay. The accuracy of these bioinformatics methods is quite high and the next step is to optimise the process by tackling the interference issue. As various analytes may have the same signals within an SRM experiment and thus interfere with each other’s signals, different solutions are being derived to tackle the issue. This thesis describes the development of a SRM transition database to store peptide and transition data, software to populate the database and also software to retrieve the data from the database. Finally the database is tested with the MRMaid transitions for the human proteome which were mined from the PRIDE database and the results analysed to investigate the transition interference issue. The database currently contains data for 20220 proteins and approximately 870,000 tryptic peptides from the human proteome

Outsourcing labour to the cloud

Various forms of open sourcing to the online population are establishing themselves as cheap, effective methods of getting work done. These have revolutionised the traditional methods for innovation and have contributed to the enrichment of the concept of 'open innovation'. To date, the literature concerning this emerging topic has been spread across a diverse number of media, disciplines and academic journals. This paper attempts for the first time to survey the emerging phenomenon of open outsourcing of work to the internet using 'cloud computing'. The paper describes the volunteer origins and recent commercialisation of this business service. It then surveys the current platforms, applications and academic literature. Based on this, a generic classification for crowdsourcing tasks and a number of performance metrics are proposed. After discussing strengths and limitations, the paper concludes with an agenda for academic research in this new area

Crowdsourcing the Collection of Transportation Behavior Data

Understanding the travel behaviors of individuals who use public transit is essential for enhancing the performance, sustainability and efficiency of public transportation. Contemporary methods for collecting data on transportation behavior are focused on manual or automated procedures for counting the number of individual passengers entering or exiting transit vehicles. While such methods provide useful data for understanding transit demand throughout a network, they ignore the important details of how passengers travel to and within a network as well as their personal experiences during their commute, all of which can enrich the ability of transit agencies to provide sustainable transportation. To address this issue, there has been a proliferation of location-based services (LBS) that allow for new methods of data collection involving passengers volunteering data about their commute. In this light, passengers engage in a crowdsourcing effort to generate data about experiences across the network. This project’s objective is to implement and test specific LBS in a bus transit network to better understand their potential and limitations for improving the crowdsourcing of travel behavior data