Detecting, tracking and counting people getting on/off a metropolitan train using a standard video camera

The main source of delays in public transport systems (buses, trams, metros, railways) takes place in their stations. For example, a public transport vehicle can travel at 60 km per hour between stations, but its commercial speed (average en-route speed, including any intermediate delay) does not reach more than half of that value. Therefore, the problem that public transport operators must solve is how to reduce the delay in stations. From the perspective of transport engineering, there are several ways to approach this issue, from the design of infrastructure and vehicles to passenger traffic management. The tools normally available to traffic engineers are analytical models, microscopic traffic simulation, and, ultimately, real-scale laboratory experiments. In any case, the data that are required are number of passengers that get on and off from the vehicles, as well as the number of passengers waiting on platforms. Traditionally, such data has been collected manually by field counts or through videos that are then processed by hand. On the other hand, public transport networks, specially metropolitan railways, have an extensive monitoring infrastructure based on standard video cameras. Traditionally, these are observed manually or with very basic signal processing support, so there is significant scope for improving data capture and for automating the analysis of site usage, safety, and surveillance. This article shows a way of collecting and analyzing the data needed to feed both traffic models and analyze laboratory experimentation, exploiting recent intelligent sensing approaches. The paper presents a new public video dataset gathered using real-scale laboratory recordings. Part of this dataset has been annotated by hand, marking up head locations to provide a ground-truth on which to train and evaluate deep learning detection and tracking algorithms. Tracking outputs are then used to count people getting on and off, achieving a mean accuracy of 92% with less than 0.15% standard deviation on 322 mostly unseen dataset video sequences.Sergio A. Velastin is grateful for funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement N 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, Cultura y Deporte (CEI-15-17) and Banco Santander. Rodrigo Fernandez and Sergio A. Velastin gratefully acknowledge the Chilean National Science and Technology Council (Conicyt) for its funding under CONICYT-Fondecyt Regular Grant Nos. 1120219, 1080381 and 1140209 (“OBSERVE”)

ECO: Egocentric Cognitive Mapping

We present a new method to localize a camera within a previously unseen environment perceived from an egocentric point of view. Although this is, in general, an ill-posed problem, humans can effortlessly and efficiently determine their relative location and orientation and navigate into a previously unseen environments, e.g., finding a specific item in a new grocery store. To enable such a capability, we design a new egocentric representation, which we call ECO (Egocentric COgnitive map). ECO is biologically inspired, by the cognitive map that allows human navigation, and it encodes the surrounding visual semantics with respect to both distance and orientation. ECO possesses three main properties: (1) reconfigurability: complex semantics and geometry is captured via the synthesis of atomic visual representations (e.g., image patch); (2) robustness: the visual semantics are registered in a geometrically consistent way (e.g., aligning with respect to the gravity vector, frontalizing, and rescaling to canonical depth), thus enabling us to learn meaningful atomic representations; (3) adaptability: a domain adaptation framework is designed to generalize the learned representation without manual calibration. As a proof-of-concept, we use ECO to localize a camera within real-world scenes---various grocery stores---and demonstrate performance improvements when compared to existing semantic localization approaches

Segmentation-guided privacy preservation in visual surveillance monitoring

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2022, Director: Sergio Escalera Guerrero, Zenjie Li i Kamal Nasrollahi[en] Video surveillance has become a necessity to ensure safety and security. Today, with the advancement of technology, video surveillance has become more accessible and widely available. Furthermore, it can be useful in an enormous amount of applications and situations. For instance, it can be useful in ensuring public safety by preventing vandalism, robbery, and shoplifting. The same applies to more intimate situations, like home monitoring to detect unusual behavior of residents or in similar situations like hospitals and assisted living facilities. Thus, cameras are installed in public places like malls, metro stations, and on-roads for traffic control, as well as in sensitive settings like hospitals, embassies, and private homes. Video surveillance has always been as- sociated with the loss of privacy. Therefore, we developed a real-time visualization of privacy-protected video surveillance data by applying a segmentation mask to protect privacy while still being able to identify existing risk behaviors. This replaces existing privacy safeguards such as blanking, masking, pixelation, blurring, and scrambling. As we want to protect human personal data that are visual such as appearance, physical information, clothing, skin, eye and hair color, and facial gestures. Our main aim of this work is to analyze and compare the most successful deep-learning-based state-of-the-art approaches for semantic segmentation. In this study, we perform an efficiency-accuracy comparison to determine which segmentation methods yield accurate segmentation results while performing at the speed and execution required for real-life application scenarios. Furthermore, we also provide a modified dataset made from a combination of three existing datasets, COCO_stuff164K, PASCAL VOC 2012, and ADE20K, to make our comparison fair and generate privacyprotecting human segmentation masks

Visual Encoding of Dissimilarity Data via Topology-Preserving Map Deformation

We present an efficient technique for topology-preserving map deformation and apply it to the visualization of dissimilarity data in a geographic context. Map deformation techniques such as value-by-area cartograms are well studied. However, using deformation to highlight (dis)similarity between locations on a map in terms of their underlying data attributes is novel. We also identify an alternative way to represent dissimilarities on a map through the use of visual overlays. These overlays are complementary to deformation techniques and enable us to assess the quality of the deformation as well as to explore the design space of blending the two methods. Finally, we demonstrate how these techniques can be useful in several—quite different—applied contexts: travel-time visualization, social demographics research and understanding energy flowing in a wide-area power-grid

Visualizing and Interacting with Geospatial Networks:A Survey and Design Space

This paper surveys visualization and interaction techniques for geospatial networks from a total of 95 papers. Geospatial networks are graphs where nodes and links can be associated with geographic locations. Examples can include social networks, trade and migration, as well as traffic and transport networks. Visualizing geospatial networks poses numerous challenges around the integration of both network and geographical information as well as additional information such as node and link attributes, time, and uncertainty. Our overview analyzes existing techniques along four dimensions: i) the representation of geographical information, ii) the representation of network information, iii) the visual integration of both, and iv) the use of interaction. These four dimensions allow us to discuss techniques with respect to the trade-offs they make between showing information across all these dimensions and how they solve the problem of showing as much information as necessary while maintaining readability of the visualization. https://geonetworks.github.io.Comment: To be published in the Computer Graphics Forum (CGF) journa

Automatic creation of schematic maps : a case study of the railway network at the Swedish Transport Administration

Idag vill man effektivisera användningen av geografiska informationssystem (GIS), vilket uppmuntrar järnvägsförvaltningarna att centralisera sina data. Därför hämtar de både geografiska och schematiska framställningar från samma databas. Men, en svårighet som möter Trafikverket är att olika representationer (geografiska och schematiska) måste uppdateras separat, vilket innebär större kostnader och kan innebära inkonsekvens i riktigheten av dessa två representationer. Således, denna studie syftar till att undersöka hur ArcGIS Schematics automatiskt kan generera den schematiska kartan från en geografisk databas som är lämplig för järnvägsapplikationer. Dessutom utvärdera den om de data som finns i den aktuella databasen som tillhandahålls av Trafikverket kan användas för att skapa schematiska kartor i ArcGIS Schematics. Resultaten visar att ArcGIS Schematics automatiskt kan skapa och uppdatera schematiska kartor över järnvägsnäten. Detta gör framställningen av schematiska kartor mycket lättare för Trafikverket, medan de för närvarande skapar de schematiska kartorna manuellt. Dessutom visar utvärderingen att av de data som används av Trafikverket för närvarande kan användas av ArcGIS Schematics, men vissa förbättringar måste göras på deras datamodell. En ny egenskap måste adderas till datamodellen, som definierar typer av olika delar av spår. Därför måste definieras om tåget är på en genom väg och åker direkt eller över från en divergerande väg som används för att ändra rutten och ansluta olika vägar. Dessutom, kontrollera topologiska kartor för att förbättra riktigheten i data har varit viktigt. Studien innehåller också en jämförelse med hur järnvägsoperatörer i Frankrike och Holland jobbar med schematiska kartor.Today, efficient use of Geographical Information Systems (GIS) encourages the railways administrations to centralize their data. Therefore, they extract both geographical and schematic representations from the same database. But, one difficulty which is encountered by the Swedish Transport Administration (Trafikverket), is that different representations (geographical and schematic) have to be updated separately which imply larger costs and there is also a rise of inconsistency between the two representations. Thus, this research aims at examining ArcGIS Schematics extension for automatically generating and updating the schematic map from a geographical database that is suitable for railway applications. In addition, it wants to evaluate the data in the current database provided by Trafikverket for creating the schematic maps as a suitable input for ArcGIS Schematics. The results show that ArcGIS Schematics extension can automatically create and update the schematic map for railway networks, and it makes the situations much easier for Trafikverket while currently they create the schematic maps manually. In addition, the evaluation of data that Trafikverket uses currently shows that ArcGIS Schematics extension is matched with their data, but some improvements must be done on their data model. It means that a definition for defining the types of different parts of tracks and switch legs regarding as main parts or excluded parts must be added to their data model. Moreover, controlling the topological maps regarding the accuracy of data has been important

Few-shot Domain Adaptation for 3D Human Pose and Shape Estimation

Department of Computer Science and EngineeringDespite recent advancements in monocular 3D human pose and shape estimation, many previous works are susceptible to the domain gap between the training data and the test data. This problem become even more severe when the test samples are from challenging in-the-wild scenarios. This paper proposes a domain adaptation approach to mitigate the gap especially in few-shot test environment, utilizing (1) continuous metric loss to constrain the feature space distance relationships between different poses, and (2) segmentation module to localize foreground area so that negative effects from noisy background can be mitigated. Our method achieved slight improvement compared to the baseline on MPI-INF-3DHP and 3DPW datasets.ope

Single view depth estimation from train images

L'estimation de la profondeur consiste à calculer la distance entre différents points de la scène et la caméra. Savoir à quelle distance un objet donné est de la caméra permettrait de comprendre sa représentation spatiale. Les anciennes méthodes ont utilisé des paires d'images stéréo pour extraire la profondeur. Pour avoir une paire d'images stéréo, nous avons besoin d'une paire de caméras calibrées. Cependant, il est plus simple d'avoir une seule image étant donnée qu'aucun calibrage de caméra n'est alors nécessaire. C'est pour cette raison que les méthodes basées sur l'apprentissage sont apparues. Ils estiment la profondeur à partir d'une seule image. Les premières solutions des méthodes basées sur l'apprentissage ont utilisé la vérité terrain de la profondeur durant l'apprentissage. Cette vérité terrain est généralement acquise à partir de capteurs tels que Kinect ou Lidar. L'acquisition de profondeur est coûteuse et difficile, c'est pourquoi des méthodes auto-supervisées se sont apparues naturellement comme une solution. Ces méthodes ont montré de bons résultats pour l'estimation de la profondeur d'une seule image. Dans ce travail, nous proposons d'estimer des cartes de profondeur d'images prises du point de vue des conducteurs de train. Pour ce faire, nous avons proposé d'utiliser les contraintes géométriques et les paramètres standards des rails pour extraire la carte de profondeur à entre les rails, afin de la fournir comme signal de supervision au réseau. Il a été démontré que la carte de profondeur fournie au réseau résout le problème de la profondeur des voies ferrées qui apparaissent généralement comme des objets verticaux devant la caméra. Cela a également amélioré les résultats de l'estimation de la profondeur des séquences des trains. Au cours de ce projet, nous avons d'abord choisi certaines séquences de trains et déterminé leurs distances focales pour calculer la carte de profondeur de la voie ferrée. Nous avons utilisé ce jeu de données et les distances focales calculées pour affiner un modèle existant « Monodepth2 » pré-entrainé précédemment sur le jeu de données Kitti.Depth prediction is the task of computing the distance of different points in the scene from the camera. Knowing how far away a given object is from the camera would make it possible to understand its spatial representation. Early methods have used stereo pairs of images to extract depth. To have a stereo pair of images, we need a calibrated pair of cameras. However, it is simpler to have a single image as no calibration or synchronization is needed. For this reason, learning-based methods, which estimate depth from monocular images, have been introduced. Early solutions of learning-based problems have used ground truth depth for training, usually acquired from sensors such as Kinect or Lidar. Acquiring depth ground truth is expensive and difficult which is why self-supervised methods, which do not acquire such ground truth for fine-tuning, has appeared and have shown promising results for single image depth estimation. In this work, we propose to estimate depth maps for images taken from the train driver viewpoint. To do so, we propose to use geometry constraints and rails standard parameters to extract the depth map inside the rails, to provide it as a supervisory signal to the network. To this end, we first gathered a train sequences dataset and determined their focal lengths to compute the depth map inside the rails. Then we used this dataset and the computed focal lengths to finetune an existing model "Monodepth2" trained previously on the Kitti dataset. We show that the ground truth depth map provided to the network solves the problem of depth of the rail tracks which otherwise appear as standing objects in front of the camera. It also improves the results of depth estimation of train sequences

An outline of the inshore submarine geology of Southern South West Africa and Namaqualand

An outline of the inshore submarine geology of the south western coast of southern Africa is presented. The study is derived from diamond prospecting operations carried out between 1964 and 1970 in the shallow waters between Walvis Bay in South West Africa and the Olifants River mouth in Namaqualand, Republic of South Africa - a distance of approximately 1 000 km (600 miles). The area can be conveniently subdivided into three regions from north to south: (i) Tidal Diamond's Concession (T.D.C.) from Sandwich Harbour to Hottentot Bay. (ii) Marine Diamond Corporation's Concession (M.D.C.) from Luderitz to the mouth of the Orange River. (iii) Southern Diamond's Concession (S.D.C.) from the Orange River mouth to the mouth of the Olifants River

The Development of a bi-level geographic information systems (GIS) database model for informal settlement upgrading

Bibliography : leaves 348-369.Existing Urban GIS models are faced with several limitations. Firstly, these models tend to be single-scale in nature. They are usually designed to operate at either metropolitan- or at the local-level. Secondly, they are generally designed to cater only for the needs of the formal and environmental sectors of the city system. These models do not cater for the "gaps" of data that exist in digital cadastres throughout the world. In the developed countries, these gaps correspond to areas of physical decay or economic decline. In the developing countries, they correspond to informal settlement areas. In this thesis, a new two-scale urban GIS database model, termed the "Bi-Ievel model" is proposed. This model has been specifically designed to address these gaps in the digital cadastre. Furthermore, the model addresses the short-comings facing current informal settlement upgrading models by providing mechanisms for community participation, project management, creating linkages to formal and environmental sectoral models, and for co-ordinating initiatives at a global-level. The Bi-Ievel model is comprised of a metropolitan-level and a series of local-level database components. These components are inter-linked through bi-directional database warehouse connections. While the model requires Internet-connectivity to achieve its full potential across a metropolitan region, it recognises the need for community participation-based methods at a local-level. Members of the community are actually involved in capturing and entering informal settlement data into the local-level database