Vehicle subtype, make and model classification from side profile video

This paper addresses the challenging domain of vehicle classification from pole-mounted roadway cameras, specifically from side-profile views. A new public vehicle dataset is made available consisting of over 10000 side profile images (86 make/model and 9 sub-type classes). 5 state-of-the-art classifiers are applied to the dataset, with the best achieving high classification rates of 98.7% for sub-type and 99.7- 99.9% for make and model recognition, confirming the assertion made that single vehicle side profile images can be used for robust classification

Data Augmentation and Clustering for Vehicle Make/Model Classification

Vehicle shape information is very important in Intelligent Traffic Systems (ITS). In this paper we present a way to exploit a training data set of vehicles released in different years and captured under different perspectives. Also the efficacy of clustering to enhance the make/model classification is presented. Both steps led to improved classification results and a greater robustness. Deeper convolutional neural network based on ResNet architecture has been designed for the training of the vehicle make/model classification. The unequal class distribution of training data produces an a priori probability. Its elimination, obtained by removing of the bias and through hard normalization of the centroids in the classification layer, improves the classification results. A developed application has been used to test the vehicle re-identification on video data manually based on make/model and color classification. This work was partially funded under the grant.Comment: Proceedings of the 2020 Computing Conference, Volume 1-3, SAI 16-17 July 2020 Londo

Methods of the Vehicle Re-identification

Most of researchers use the vehicle re-identification based on classification. This always requires an update with the new vehicle models in the market. In this paper, two types of vehicle re-identification will be presented. First, the standard method, which needs an image from the search vehicle. VRIC and VehicleID data set are suitable for training this module. It will be explained in detail how to improve the performance of this method using a trained network, which is designed for the classification. The second method takes as input a representative image of the search vehicle with similar make/model, released year and colour. It is very useful when an image from the search vehicle is not available. It produces as output a shape and a colour features. This could be used by the matching across a database to re-identify vehicles, which look similar to the search vehicle. To get a robust module for the re-identification, a fine-grained classification has been trained, which its class consists of four elements: the make of a vehicle refers to the vehicle's manufacturer, e.g. Mercedes-Benz, the model of a vehicle refers to type of model within that manufacturer's portfolio, e.g. C Class, the year refers to the iteration of the model, which may receive progressive alterations and upgrades by its manufacturer and the perspective of the vehicle. Thus, all four elements describe the vehicle at increasing degree of specificity. The aim of the vehicle shape classification is to classify the combination of these four elements. The colour classification has been separately trained. The results of vehicle re-identification will be shown. Using a developed tool, the re-identification of vehicles on video images and on controlled data set will be demonstrated. This work was partially funded under the grant.Comment: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys) Volume 1-3, 3-4 Sep. 2020 Amsterda

Observed Risk and User Perception of Road Infrastructure Safety Assessment for Cycling Mobility

The opportunities for data collection in smart cities and communities provide new approaches for assessing risk of roadway components. This paper presents and compares two different methodological approaches for cycling safety assessment of objective and perceived risk. Objective risk was derived from speed and direction profiles collected with Global Navigation Satellite System (GNSS) and camera installed on an instrumented bicycle. Safety critical events between cyclists and other road users were identified and linked to five different roadway components. A panel of experts was asked to score the severity of the safety critical events using a Delphi process to reach consensus. To estimate the perceived risk, a web-based survey was provided to the city bicyclist community asking them to score the same five roadway components with a 4-point Likert scale. A comparison between perceived and objective risk classification of the roadway components showed a good agreement when only higher severity conflicts were considered. The research findings support the notion that it is possible to collect information from bicycle probe data that match and user perceptions and thus, utilizing them to take advantage of such data in advancing the goals of in smart cities and communities

Siamese networks for surveillance and security

This thesis investigates the usage of Siamese networks across three surveillance and security tasks for land border security. Siamese networks (also known as a twin-pair network) are a layout of neural networks that contain a segment that contains duplicated architecture and configuration parameters for feature extraction of two inputs, combining the outputs into one vector for comparison in a final set of layers to produce a similarity score. The effectiveness of multiple architectures of Siamese networks crafted from multiple generations of Convolutional Neural Networks and Residual Neural Networks are examined for side-profile vehicle classification and Differential Morphing Attack Detection (D-MAD), and with a novel architecture for trajectory similarity analysis. The challenging domain of automated vehicle classification from pole-mounted roadway cameras from side-profile views is evaluated. Three Siamese networks based on existing non-Siamese architectures are proposed and compared against five existing methods on a novel and published dataset. The evaluation undertaken shows that the residual based Siamese network is able to outperform other state of the art methods on datasets with a small number of classes. An end-to-end Siamese trajectory network framework is proposed for the purpose of trajectory similarity analysis in surveillance tasks. A deep feature auto-encoding network is used as part of a discriminative Siamese architecture to perform trajectory similarity analysis. The effectiveness of this method is evaluated on four challenging public real-world datasets containing both vehicle and pedestrian targets, and compared with five existing methods. The proposed method outperforms the existing methods on three of the four datasets. Face morphing attacks pose an increasingly severe threat to automatic face recognition systems in border control environments. Three Siamese architectures built up from multiple generations of non-Siamese Convolutional and Residual Neural Networks for D-MAD are proposed, showing the effectiveness of these networks against a pre-established Convolutional architecture for Single-image Morphing Attack Detection (S-MAD). The residual network based architecture outperforms representative convolutional architectures from the literature, with the Siamese D-MAD architecture able to outperform its S-MAD variant

STELLAR (Semantic Technologies Enhancing the Lifecycle of Learning Resources): Jisc Final Report

[Project Summary] As one of the earliest distance learning providers The Open University (OU) has a rich heritage of archived learning materials. An ever increasing amount of that is in digital form and is being deposited with the University Archive. This growth has been driven by digitisation activity from projects such as AVA (Access to Video Assets) and the Fedora-based Open University Digital Library ‘a place to discover digital and digitised archival content from the OU Library, from videos and images to digitised documents’. Other digital content is being captured from web archiving activities, such as work to preserve Moodle Virtual Learning Environment course websites. An evidence based understanding is required to inform digital preservation policies, curation strategy and investment in digital library development. Following the Pre-enhancement, Enhancement and Post-enhancement methodology set out by Jisc, STELLAR adopted the model of a balanced scorecard to ascertain the value ascribed to the non-current learning materials. Four aspects were considered: Personal and professional perspectives of value; Value to the Higher Educational and academic communities; Value to internal processes and cultures; Financial perspectives of value. The outcomes of the survey indicated that stakeholders place a high value on the materials, and that they perceived them to have value in all areas evaluated. Three OU courses were chosen from the digital library for the transformation stage. These materials were enhanced and transformed into RDF, a process that required more extensive metadata expertise and effort than was expected. Following enhancement the RDF was accessed through a tool called DiscOU, created by a member of the project team from the OU’s Knowledge Media Institute. DiscOU uses both linked data and a semantic meaning engine to analyse the meaning of the text in a search query. This is matched against the meaning of the content derived from an index of the full-text of the digital library content. In the final stage stakeholders were asked through a survey and series of workshops to use the DiscOU proof-of-concept tool to assess their perception of the value of this transformation. This has revealed that overall, academics and other stakeholders in the university do believe that the value of the selected materials was positively impacted by the application of semantic technologies

Development of New Treatments for Asthma and Neuropathic Pain Based on Ɣ-Aminobutyric Acid a Receptor (GABAAR) Ligands

The γ-aminobutyric acid A receptor (GABAAR) is a ligand-gated pentameric chloride channel consisting of several identified subunits: α1-6, β1-3, γ1-3, δ, ε, π, θ, ρ1-3.1-2 Typical arrangement of subunits consists of two α subunits, two β subunits, and one γ subunit.3 GABAARs have two binding sites for the endogenous ligand γ-aminobutyric acid (GABA), between the α and β subunits. GABAARs also have a binding site for positive allosteric modulators, such as benzodiazepines, between the α and γ subunits.4-5 Due to their ability to treat anxiety, epilepsy, insomnia, and muscle relaxation, benzodiazepines are widely prescribed pharmaceuticals.6-7 Still, adverse effects result from benzodiazepine use, including but not limited to: sedation, impaired motor coordination, amnesia, tolerance, dependence, and severe withdrawal symptoms.6, 8 Selectivity of benzodiazepines to specific GABAAR subtypes is currently well accepted drug discovery strategy.8 The rotarod assay has been used for over 70 years to quantify the neurological effects of muscle relaxants, convulsants, and central nervous system (CNS) depressants.12 It is a reliable and sensitive in vivo sensorimotor assay that can detect neurological deficits such as sedation or impaired motor coordination.¹² Additionally, an open field test can be performed in parallel to provide a more robust analysis of neurological deficits. The Arnold Group has used these assays to screen hundreds of novel subtype-selective imidazodiazepines to identify those without adverse CNS effects. Imidazodiazepines demonstrating no adverse CNS effects were used to develop leads to treat asthma inflammation and neuropathic pain by targeting non-neuronal cells expressing a narrow subset of GABAAR subunits. 9-11 T-lymphocytes, alveolar macrophages, and eosinophils¹⁷ all have been shown to express functional GABAARs suggesting their role in the airway inflammatory response.13⁻15 CD4+ T-lymphocytes are of significant interest for their role in stimulating and coordinating the airway inflammatory immune response.14 Here, we describe the relationship between imidazodiazepines targeting non-neuronal immune cells and the corresponding airway inflammatory response both in vivo and in vitro. Airway hyperresponsiveness (AHR) was investigated in methacholine-challenged murine models in vivo using a non-invasive airway mechanics (NAM) plethysmograph. Methacholine, a cholinergic drug, acts on muscarinic receptors and causes narrowing of airways similar to asthma.¹⁶ The NAM instrument measures pressure, volume, and frequency of respiration and combines these parameters into an sRaw value measured in cmH2O*sec. Several novel imidazodiazepines were identified to have lower sRaw values in this model compared to commercially available asthma therapeutics. This trend was observed in studies of repeat dose and prophylactic dose for orally and nebulized delivered compounds, as well as for rescue inhalant studies. Lead compounds were also subject to in vitro experimentation. CD4+ T-cells have a well understood role in asthma and are responsible for perpetuating the inflammatory response by infiltrating airways and secreting inflammatory cytokines.¹⁸ The cytokine release directs other cell types to mediate many of the clinical characteristics of asthma, including heightened IgE production, increased eosinophilia, and accelerated immune cell proliferation.¹⁹ This complex inflammatory reaction can be classified into Th1 or Th2 responses. Th1 responses are characterized by cytokines like interferon γ (IFNγ), interleukin-2 (IL-2), and lymphotoxin (LT), which promote the cell-mediated immune response.²⁰ The Th2 immune response is characterized by production of multiple cytokines: IL-4, IL-5, IL-10, and IL-13, among others.²⁰ To investigate the anti-inflammatory effects of GABAAR ligands, a protocol was developed to quantify the reduction of IL-5 and IL-13 production by primary activated CD4+ T-cells isolated from female Swiss Webster splenocytes using rtPCR. Functional γ-aminobutyric acid type A receptors (GABAARs) are well-characterized in neurons and have been discovered on glial cells. This includes α1, α3, and β1 subunits found on mouse microglia,²¹ which have been reported to mediate immune signaling.²² Previous anti-inflammatory studies with novel α2/α3-subtype GABAAR positive allosteric modulators have shown analgesic properties.²⁵,²⁶ Taken together, these results suggest that novel imidazodiazepines are neuropathic pain lead compounds, reducing inflammation through targeting CNS microglia, and providing an analgesic effect. A murine formalin test was performed to investigate in vivo compound efficacy in reducing neuropathic pain

Investigating the mechanisms of action of phytocannabinoids and a novel cognitive enhancer to target the comorbidity of temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common type of epilepsy and exists with memory loss as a comorbidity. The conventional therapy available to treat these disorders achieves only modest therapeutic efficacy at best. This study investigates two potential treatments: phytocannabinoids to alleviate seizures, and a novel cognitive enhancer to restore/halt memory deficits. The anti-convulsant properties of cannabidiol (CBD) were first examined with regards to the neuropathology of two major types of hippocampal interneurons expressing parvalbumin (PV) and cholecystokinin (CCK) which are thought to dysfunction during epilepsy. Immunohistochemistry experiments using an in vivo kainic-acid induced epileptic rat model, revealed that PV- and CCK-immunopositive interneurons were significantly affected during epilepsy. This effect was greatly reduced following CBD treatment, suggesting that CBD exerts a neuroprotective function. The effects of CBD on the intrinsic membrane properties of these interneurons, together with hippocampal pyramidal cells, were further investigated in acute brain slices of rat seizure models of TLE (in vivo kainic acid-induced and in vitro Mg2+ free-induced). Whole-cell recordings revealed that bath application of CBD (10 µM) normalised the firing frequency of epileptic adapting pyramidal cells to healthy control levels. A similar effect was seen in hippocampal CCK-immunopositive Schaffer collateral associated (SCA) interneurons. In contrast, CBD resulted in an increased firing of PV-immunopositive interneurons, thus increasing their excitability and restoring the impaired membrane properties of the cells apparent in the epileptic models. The effects of cannabidivarin (CBDV), a similar cannabinoid compound, on the intrinsic membrane properties of these cell types were also evaluated. Additionally, CBDV affected excitatory postsynaptic currents by reducing excitation. In an attempt to address the memory impairment aspect associated with TLE, I investigated the neuronal effects of a5AM21, a novel potential memory enhancer. Electrophysiological experiments revealed that a5AM21 preferentially acts on 5-containing gamma (γ)-aminobutyric acid (GABA) type A (GABAA) receptors, reducing their inhibitory effects. Furthermore, data obtained using behavioural experiment paradigm, the eight-arm radial maze, suggest a significant improvement in short- and long-term memory retrieval in rats treated with a5AM21. In conclusion, the results reveal the potential mechanisms of action of two therapies to alleviate seizures and memory impairment, and the future goals would be to combine CBD/CBDV and a5AM21 as a promising novel targeted therapy for TLE

Explaining Deep Learning-Based Driver Models

Different systems based on Artificial Intelligence (AI) techniques are currently used in relevant areas such as healthcare, cybersecurity, natural language processing, and self-driving cars. However, many of these systems are developed with 'black box” AI, which makes it difficult to explain how they work. For this reason, explainability and interpretability are key factors that need to be taken into consideration in the development of AI systems in critical areas. In addition, different contexts produce different explainability needs which must be met. Against this background, Explainable Artificial Intelligence (XAI) appears to be able to address and solve this situation. In the field of automated driving, XAI is particularly needed because the level of automation is constantly increasing according to the development of AI techniques. For this reason, the field of XAI in the context of automated driving is of particular interest. In this paper, we propose the use of an explainable intelligence technique in the understanding of some of the tasks involved in the development of advanced driver-assistance systems (ADAS). Since ADAS assist drivers in driving functions, it is essential to know the reason for the decisions taken. In addition, trusted AI is the cornerstone of the confidence needed in this research area. Thus, due to the complexity and the different variables that are part of the decision-making process, this paper focuses on two specific tasks in this area: the detection of emotions and the distractions of drivers. The results obtained are promising and show the capacity of the explainable artificial techniques in the different tasks of the proposed environments.This work was supported under projects PEAVAUTO-CM-UC3M, PID2019-104793RB-C31, and RTI2018-096036-B-C22, and by the Region of Madrid’s Excellence Program (EPUC3M17)