Literatura académica sobre el tema "Dashcam"

A major problem in metropolitan areas is finding parking spaces. Existing parking guidance systems often adopt fixed sensors or cameras that cannot provide information from the driver’s point of view. Motivated by the advent of dashboard cameras (dashcams), we develop neural-network-based methods for detecting vacant parking spaces in videos recorded by a dashcam. Detecting vacant parking spaces in dashcam videos enables early detection of spaces. Different from conventional object detection methods, we leverage the monotonicity of the detection confidence with respect to the distance away of the approaching target parking space and propose a new loss function, which can not only yield improved detection results but also enable early detection. To evaluate our detection method, we create a new large dataset containing 5,800 dashcam videos captured from 22 indoor and outdoor parking lots. To the best of our knowledge, this is the first and largest driver’s view video dataset that supports parking space detection and provides parking space occupancy annotations.

In recent years, there has been a huge boom in the market in terms of both supply and demand for dashcams. This stems from the purpose of the camera installed in the vehicle. Dashcams are currently becoming an important aid in solving various adverse incidents, such as detecting violations of traffic regulations and other laws and recording and clarifying the course of a traffic accident or other criminologically relevant event. The purpose of this camera is to monitor the space in front and behind the vehicle, or the situation inside the vehicle. The content of this article is aimed at acquainting the reader with basic information about dashcams. Specifically, the individual parts of the article will discuss the importance and significance of the dashcams, the benefits of its use, and the legislative framework that regulates its use. The paper will conclude with a brief overview of innovative approaches based on the application of a dashcam for the purpose of increasing the level of safety of road users while driving a vehicle.

The article offers a method for determining vehicle speed from the audio track recorded by the dashboard camera mounted on the car in question. The author analyzed the operation of a four-stroke internal combustion engine, and established a dependency between the number of rotations (rpm) of the engine crankshaft and the number of rotations transferred to the wheel by the manual transmission. The author presents a formula that helps to determine the static radius of the wheel from its marking symbols on the tire. He looks at examples from forensic casework and offers options for determining the ID number of an engaged gearbox. Furthermore, he reviews the possibilities of applying this method to vehicles with an automatic transmission. In conclusion, the article presents the results of tests of the proposed method conducted by leading experts from various forensic institutions of the Russian Ministry of Justice. They point to the consistency of results when engine rpm is calculated based on cepstral analysis and engine tachometer readings.

Verkehr: Es sind oft die kleinen Dinge, die Autofahrern eine wichtige Unterstützung sein können. Die VDI nachrichten haben sich auf die Suche nach solchen Helfern gemacht und stellen einige vor – von der Dashcam über Ladegeräte bis zur Starthilfe. Von Peter Kellerhoff

Med en ökad efterfrågan av övervakningskameror på marknaden ökar samtidigt efterfrågan för olika monteringsmöjligheter. I andra länder, utanför Sverige, användskameror för att filma vägen inifrån fordonen, vilket nu håller på att introduceras iSverige. En sådan kamera sitter exempelvis på instrumentbrädan eller på vindrutanoch kallas dashcam. Detta är en ny marknad inom Sverige och omfattar trafikövervakning via fordon. Detta examensarbete utförs på Malmö universitet i samarbetemed Axis Communications AB. Syftet med detta examensarbete är att utforma enfast montering av en kamera på insidan av vindrutan i fordon för att fungera som endashcam, genom att använda olika processer av produktutveckling och andra metoder. Målet är att utveckla en prototyp som ska fungera som ett underlag för framtidautveckling. Konceptet som valdes i detta projekt fokuserar på en helhetslösning föratt kunna filma ut ur fordon med acceptabel bildkvalité. Detta resulterade i en prototyp som monteras på insidan av ett fordon mot vindrutan med en trattlösning föratt minimera reflektioner från oönskat ljus för att åstadkomma en bra bild. Genomavgränsningar i projektet har en bredare helhetslösning utvecklats.Syftet och målet för projektet anses vara uppfyllda då de flesta krav enligt önskadekundbehov har blivit uppfyllda och prototypen är anpassad för de flest önskvärdafunktioner. Arbetet har fokuserat på att kommentera punkter värt att undersöka förframtida utveckling.
With an increased demand on the market for surveillance cameras, there is a increaseddemand at the same time for different mounting possibilities. In other countries, outside of Sweden, the usage of cameras has been applied to record the road from insideof the vehicle, this is now presented in Sweden. Said camera is usually installed onthe dashboard or windshield and is called a dashcam. This is a new market withinSweden and involves traffic surveillance by vehicle. This thesis is performed at MalmöUniversity in collaboration with Axis Communications AB. The purpose of this thesisis to develop a solid mounting of a camera on the inside of a windshield of a vehicle to work as a dashcam, by using different processes of product development andother methods. The goal is to develop a prototype to be used as a basis for futuredevelopments. The concept chosen in this project focus on the total solution for beingable to record from the inside of the vehicle with an acceptable recording quality.This resulted in a prototype which is mounted on the inside of the vehicle againstthe windshield with a funnel solution to minimize the reflections from unwanted lightto achieve a good recording quality. By delimitations of the project a broader totalsolution could be developed.The purpose and goal for this project is considered fulfilled. As most requirementsaccording to the desired customer needs is fulfilled and the prototype is adapted tothe most desired functions. The thesis has also focused on commenting subjects worthinvestigating in future development.

In the last 50 years, flooding has figured as the most frequent and widespread natural disaster globally. Extreme precipitation events stemming from climate change could alter the hydro-geological regime resulting in increased flood risk. Near real-time precipitation monitoring at local scale is essential for flood risk mitigation in urban and suburban areas, due to their high vulnerability. Presently, most of the rainfall data is obtained from ground‐based measurements or remote sensing that provide limited information in terms of temporal or spatial resolution. Other problems may be due to the high costs. Furthermore, rain gauges are unevenly spread and usually placed away from urban centers. In this context, a big potential is represented by the use of innovative techniques to develop low-cost monitoring systems. Despite the diversity of purposes, methods and epistemological fields, the literature on the visual effects of the rain supports the idea of camera-based rain sensors but tends to be device-specific. The present thesis aims to investigate the use of easily available photographing devices as rain detectors-gauges to develop a dense network of low-cost rainfall sensors to support the traditional methods with an expeditious solution embeddable into smart devices. As opposed to existing works, the study focuses on maximizing the number of image sources (like smartphones, general-purpose surveillance cameras, dashboard cameras, webcams, digital cameras, etc.). This encompasses cases where it is not possible to adjust the camera parameters or obtain shots in timelines or videos. Using a Deep Learning approach, the rainfall characterization can be achieved through the analysis of the perceptual aspects that determine whether and how a photograph represents a rainy condition. The first scenario of interest for the supervised learning was a binary classification; the binary output (presence or absence of rain) allows the detection of the presence of precipitation: the cameras act as rain detectors. Similarly, the second scenario of interest was a multi-class classification; the multi-class output described a range of quasi-instantaneous rainfall intensity: the cameras act as rain estimators. Using Transfer Learning with Convolutional Neural Networks, the developed models were compiled, trained, validated, and tested. The preparation of the classifiers included the preparation of a suitable dataset encompassing unconstrained verisimilar settings: open data, several data owned by National Research Institute for Earth Science and Disaster Prevention - NIED (dashboard cameras in Japan coupled with high precision multi-parameter radar data), and experimental activities conducted in the NIED Large Scale Rainfall Simulator. The outcomes were applied to a real-world scenario, with the experimentation through a pre-existent surveillance camera using 5G connectivity provided by Telecom Italia S.p.A. in the city of Matera (Italy). Analysis unfolded on several levels providing an overview of generic issues relating to the urban flood risk paradigm and specific territorial questions inherent with the case study. These include the context aspects, the important role of rainfall from driving the millennial urban evolution to determining present criticality, and components of a Web prototype for flood risk communication at local scale. The results and the model deployment raise the possibility that low‐cost technologies and local capacities can help to retrieve rainfall information for flood early warning systems based on the identification of a significant meteorological state. The binary model reached accuracy and F1 score values of 85.28% and 0.86 for the test, and 83.35% and 0.82 for the deployment. The multi-class model reached test average accuracy and macro-averaged F1 score values of 77.71% and 0.73 for the 6-way classifier, and 78.05% and 0.81 for the 5-class. The best performances were obtained in heavy rainfall and no-rain conditions, whereas the mispredictions are related to less severe precipitation. The proposed method has limited operational requirements, can be easily and quickly implemented in real use cases, exploiting pre-existent devices with a parsimonious use of economic and computational resources. The classification can be performed on single photographs taken in disparate conditions by commonly used acquisition devices, i.e. by static or moving cameras without adjusted parameters. This approach is especially useful in urban areas where measurement methods such as rain gauges encounter installation difficulties or operational limitations or in contexts where there is no availability of remote sensing data. The system does not suit scenes that are also misleading for human visual perception. The approximations inherent in the output are acknowledged. Additional data may be gathered to address gaps that are apparent and improve the accuracy of the precipitation intensity prediction. Future research might explore the integration with further experiments and crowdsourced data, to promote communication, participation, and dialogue among stakeholders and to increase public awareness, emergency response, and civic engagement through the smart community idea.
Negli ultimi 50 anni, le alluvioni si sono confermate come il disastro naturale più frequente e diffuso a livello globale. Tra gli impatti degli eventi meteorologici estremi, conseguenti ai cambiamenti climatici, rientrano le alterazioni del regime idrogeologico con conseguente incremento del rischio alluvionale. Il monitoraggio delle precipitazioni in tempo quasi reale su scala locale è essenziale per la mitigazione del rischio di alluvione in ambito urbano e periurbano, aree connotate da un'elevata vulnerabilità. Attualmente, la maggior parte dei dati sulle precipitazioni è ottenuta da misurazioni a terra o telerilevamento che forniscono informazioni limitate in termini di risoluzione temporale o spaziale. Ulteriori problemi possono derivare dagli elevati costi. Inoltre i pluviometri sono distribuiti in modo non uniforme e spesso posizionati piuttosto lontano dai centri urbani, comportando criticità e discontinuità nel monitoraggio. In questo contesto, un grande potenziale è rappresentato dall'utilizzo di tecniche innovative per sviluppare sistemi inediti di monitoraggio a basso costo. Nonostante la diversità di scopi, metodi e campi epistemologici, la letteratura sugli effetti visivi della pioggia supporta l'idea di sensori di pioggia basati su telecamera, ma tende ad essere specifica per dispositivo scelto. La presente tesi punta a indagare l'uso di dispositivi fotografici facilmente reperibili come rilevatori-misuratori di pioggia, per sviluppare una fitta rete di sensori a basso costo a supporto dei metodi tradizionali con una soluzione rapida incorporabile in dispositivi intelligenti. A differenza dei lavori esistenti, lo studio si concentra sulla massimizzazione del numero di fonti di immagini (smartphone, telecamere di sorveglianza generiche, telecamere da cruscotto, webcam, telecamere digitali, ecc.). Ciò comprende casi in cui non sia possibile regolare i parametri fotografici o ottenere scatti in timeline o video. Utilizzando un approccio di Deep Learning, la caratterizzazione delle precipitazioni può essere ottenuta attraverso l'analisi degli aspetti percettivi che determinano se e come una fotografia rappresenti una condizione di pioggia. Il primo scenario di interesse per l'apprendimento supervisionato è una classificazione binaria; l'output binario (presenza o assenza di pioggia) consente la rilevazione della presenza di precipitazione: gli apparecchi fotografici fungono da rivelatori di pioggia. Analogamente, il secondo scenario di interesse è una classificazione multi-classe; l'output multi-classe descrive un intervallo di intensità delle precipitazioni quasi istantanee: le fotocamere fungono da misuratori di pioggia. Utilizzando tecniche di Transfer Learning con reti neurali convoluzionali, i modelli sviluppati sono stati compilati, addestrati, convalidati e testati. La preparazione dei classificatori ha incluso la preparazione di un set di dati adeguato con impostazioni verosimili e non vincolate: dati aperti, diversi dati di proprietà del National Research Institute for Earth Science and Disaster Prevention - NIED (telecamere dashboard in Giappone accoppiate con dati radar multiparametrici ad alta precisione) e attività sperimentali condotte nel simulatore di pioggia su larga scala del NIED. I risultati sono stati applicati a uno scenario reale, con la sperimentazione attraverso una telecamera di sorveglianza preesistente che utilizza la connettività 5G fornita da Telecom Italia S.p.A. nella città di Matera (Italia). L'analisi si è svolta su più livelli, fornendo una panoramica sulle questioni relative al paradigma del rischio di alluvione in ambito urbano e questioni territoriali specifiche inerenti al caso di studio. Queste ultime includono diversi aspetti del contesto, l'importante ruolo delle piogge dal guidare l'evoluzione millenaria della morfologia urbana alla determinazione delle criticità attuali, oltre ad alcune componenti di un prototipo Web per la comunicazione del rischio alluvionale su scala locale. I risultati ottenuti e l'implementazione del modello corroborano la possibilità che le tecnologie a basso costo e le capacità locali possano aiutare a caratterizzare la forzante pluviometrica a supporto dei sistemi di allerta precoce basati sull'identificazione di uno stato meteorologico significativo. Il modello binario ha raggiunto un'accuratezza e un F1-score di 85,28% e 0,86 per il set di test e di 83,35% e 0,82 per l'implementazione nel caso di studio. Il modello multi-classe ha raggiunto un'accuratezza media e F1-score medio (macro-average) di 77,71% e 0,73 per il classificatore a 6 vie e 78,05% e 0,81 per quello a 5 classi. Le prestazioni migliori sono state ottenute nelle classi relative a forti precipitazioni e assenza di pioggia, mentre le previsioni errate sono legate a precipitazioni meno estreme. Il metodo proposto richiede requisiti operativi limitati, può essere implementato facilmente e rapidamente in casi d'uso reali, sfruttando dispositivi preesistenti con un uso parsimonioso di risorse economiche e computazionali. La classificazione può essere eseguita su singole fotografie scattate in condizioni disparate da dispositivi di acquisizione di uso comune, ovvero da telecamere statiche o in movimento senza regolazione dei parametri. Questo approccio potrebbe essere particolarmente utile nelle aree urbane in cui i metodi di misurazione come i pluviometri incontrano difficoltà di installazione o limitazioni operative o in contesti in cui non sono disponibili dati di telerilevamento o radar. Il sistema non si adatta a scene che sono fuorvianti anche per la percezione visiva umana. I limiti attuali risiedono nelle approssimazioni intrinseche negli output. Per colmare le lacune evidenti e migliorare l'accuratezza della previsione dell'intensità di precipitazione, sarebbe possibile un'ulteriore raccolta di dati. Sviluppi futuri potrebbero riguardare l'integrazione con ulteriori esperimenti in campo e dati da crowdsourcing, per promuovere comunicazione, partecipazione e dialogo aumentando la resilienza attraverso consapevolezza pubblica e impegno civico in una concezione di comunità smart.

The granite – greenstone terranes in northwestern Ontario contain oval granitoid bodies. A specific granitoid body known as the Dashwa Gneiss was studied and the results are presented in this paper. The rocks in the field area display key features of diapirism that include concentric, steeply dipping foliations, radially distributed lineations and a shear zone bounding the granitoid that consistently shows 'granitoid up' kinematics relative to surrounding metavolcanic rocks. Based on these structural features, it is proposed that the oval map pattern of this region resulted from diapirism of the Dashwa Gneiss. A granitic intrusion at the core of the oval pattern is interpreted as providing the heat source responsible for lowering the viscosity of the Dashwa Gneiss sufficiently for the dense supracrustal rocks overlying the Dashwa Gneiss to trigger diapirism. The steep dip and concentric pattern of the Dashwa Gneiss indicate a pipe-like body that is attributed to erosion of the area exposing only the stem of the diapir. Mean foliation data show that the Western Wabigoon terrane is probably a diapiric field containing diapirs of variable maturity, shape, size and vertical level.
Les terranes de granite et roches vertes du nord-ouest ontarien contiennent des masses de granitoïdes à motif ovale. Un granitoïde nommé gneiss de Dashwa fut étudié et les résultats sont présentés dans cet article. Les roches sur le terrain montrent des caractéristiques importantes de diapirisme qui incluent une foliation concentrique à fort plongement, une linéation à distribution radiale et une zone de cisaillement bordant le granitoïde, qui montre de façon consistante une cinématique de 'granitoïde vers le haut'. En se basant sur ces caractéristiques structurales, il est proposé que le motif en plan ovale de cette région résulte du diapirisme du gneiss de Dashwa. L'intrusion granitique au coeur du motif ovale est interprétée comme étant la source de chaleur responsable d'une diminution de la viscosité du gneiss de Dashwa, au point où des roches métavolcaniques mafiques et denses recouvrant le gneiss de Dashwa ont activé le diapirisme. Le fort plongement et le motif concentrique du gneiss de Dashwa suggèrent une géométrie en conduit qui a été attribuée à l'érosion de la région exposant seulement le tronc du diapir. Les données de foliation moyenne indiquent que la terrane de Western Wabigoon est un champ diapirique contenant des diapirs de maturité, forme, taille et niveau vertical variables.

Uppsatsen är en historiografisk undersökning. Den granskar forskning som bedömer de armeniska massakrerna som folkmord och forskning som ifrågasätter detta. Händelserna är mycket omdiskuterade båda politiskt och historiskt. En bakgrundsöversikt belyser de sista tre decennierna av det osmanska rikets moderna historia och förspelet till händelserna under första världskriget samt påverkan av stormaktspolitiken under senare delen 1800-talet och början av 1900-talet på utvecklingen i Mellanöstern och främst inom det osmanska imperiet. Uppsatsen diskuterar vilken roll turkisk nationalism, turanism och panislamism spelade i händelserna och hur dessa påverkade relationen mellan ungturkrörelsen och armenierna. Hur tolkar forskarna källorna och vilken roll spelar källmaterialet i rekonstrueringen av historiska fakta bakom debatten om mordet på den osmanskt armeniska befolkningen 1915? Analyserna omfattar flera åsikter och attityder inom genocidforskning; det finns historiker och andra forskare som förnekar och förkastar idén att det var genocid; andra vill ha ett internationellt erkännande av massakrerna i Anatolien och klarhet om den turkiska statens skuld i denna fråga. Syftet med uppsatsen är att klarlägga förklaringarna och slutsatserna de utvalda forskarna har kommit fram till i sina analyser. Uppsatsen varken anklagar eller tar ställning för eller emot rubriceringen av massakrerna och förhåller sig neutral i största möjliga mån. Nyckelord: armenier, dashnak, hunchak, massakrer, ungturkar, ittihadister, CUP, turkism, turkisk nationalism, Turan och turanism, revolution, deportationer, omlokalisering, uppror, skuld, förnekelse och erkännande.

碩士
國立清華大學
電機工程學系所
105
We propose a Dynamic-Spatial-Attention (DSA) Recurrent Neural Network (RNN) for anticipating accidents in dashcam videos (Fig. 1.1). Our DSA-RNN learns to (1) distribute soft-attention to candidate objects dynamically to gather subtle cues and (2) model the temporal dependencies of all cues to robustly anticipate an accident. Anticipating accidents is much less addressed than anticipating events such as changing a lane, making a turn, etc., since accidents are rare to be observed and can happen in many different ways mostly in a sudden. To overcome these challenges, we (1) utilize state-of-the-art object detector [1] and tracking-by-detection [2] to detect and track candidate objects, and (2) incorporate full-frame and object-based appearance and motion features in our model. We also harvest a diverse dataset of 968 dashcam accident videos on the web (Fig.5.1). The dataset is unique, since various accidents (e.g., a motorbike hits a car, a car hits another car, etc.) occur in all videos. We manually mark the time-location of accidents and use them as supervision to train and evaluate our method. We show that our method anticipates accidents about 1.22 seconds before they occur with 80% recall and 46.92% precision. Most importantly, it achieves the highest mean average precision (63.98\%) outperforming other baselines without attention or RNN.

This chapter examines the role of notions of gender propriety in differentiating access to Armenians women's organizations in Bourj Hammoud. It focuses on the work of two distinct types of institutions—a transnational Armenian NGO and the various women's organizations affiliated with the Armenian Dashnag Party. A closer look at these organizations show how gender, particularly the performance of normative notions of gender roles and gendered propriety, enables or disables access to the networks that produce the Armenian community in various forms. Access to these channels of services and their attendant resources differs based on women's abilities to mobilize gender, kinship, and family relations, particular kinds of class positions and professional training, linguistic skills, and even spatial, neighborhood connections. Gender propriety and class positioning allow women to connect social infrastructures, to network into other networks glossed as Armenian middle class or Dashnag Party base.

For this session we welcomed Dr. Dasha Nicholls to share her knowledge and insights into eating disorders, specifically exploring the signs and impact of disordered eating and guidance on how best to respond.