Global ETD Search

31	Rastreamento veicular em vídeos de tráfego com aplicação em contagem de veículos Silva, Christiano Bouvié da January 2012 (has links) Esta dissertação apresenta o desenvolvido de um método, baseado em agrupamento de partículas, para realizar a contagem de veículos em vídeos de tráfego. Tal procedimento é importante em sistemas de tráfego inteligentes, ou como uma ferramenta auxiliar no planejamento de vias urbanas. Utilizando técnicas de processamento de imagens e agrupamento de partículas, o método proposto utiliza-se da coerência de movimento e posição espacial existente entre partículas extraídas das imagens de vídeo para agrupá-las, formando figuras convexas que são analisadas em busca de possíveis veículos. Essa análise leva em consideração a morfologia das figuras convexas e a informação de fundo da imagem, para unir ou dividir os agrupamentos. Após a identificação de um veículo, o mesmo é rastreado utilizando-se similaridade de histograma de cores, aplicado em janelas centradas nas partículas. A contagem dos veículos ocorre em laços virtuais definidos pelo usuário nas pistas desejadas, através da intersecção das figuras convexas rastreadas com estes laços virtuais. Testes foram realizados utilizando-se vídeos de seis cenas diferentes, totalizando 81.000 quadros. Os resultados das contagens de veículos obtidos foram comparados a dois métodos atuais. Um método possui abordagem similar ao método proposto (KIM, 2008), que tenta fixar agrupamentos de partículas em formas elipsoidais. O outro método (SÁNCHEZ et al., 2011) rastreia objetos conectados, quando estes são diferentes do fundo, através da intersecção destes objetos entre quadros adjacentes. Considerando-se o universo total de veículos analisados, 1085 veículos, os resultados obtidos pelo método proposto apresentaram uma diferença absoluta na contagem dos veículos intermediária aos métodos comparativos, 53 veículos contra 66 e 27 para (KIM, 2008) e (SÁNCHEZ et al., 2011) respectivamente, sendo o único método que contou menos veículos que o valor real, enquanto os métodos comparativos contaram veículos além do valor real. O método proposto perde 102 veículos, valor inferior ao método de (SÁNCHEZ et al., 2011), 181, e praticamente o mesmo número que o método de (KIM, 2008), 101. Já os veículos detectados mais de uma vez apresentam valores inferiores para o método proposto, 49, em relação aos métodos comparativos, 167 para (KIM, 2008) e 208 para (SÁNCHEZ et al., 2011). / This dissertation presents the developed of a method based on particle group, to conduct the count of vehicles in traffic videos. This procedure is important in intelligent traffic systems, or as an auxiliary tool in the planning of urban streets. Using image processing techniques and grouping of particles, the proposed method uses the coherence of spatial position and movement between particles extracted from the video footage to assemble them into convex figures that are parsed in search of possible vehicles. This analysis takes into account the morphology of convex figures and background information of the image, to merge or split the groups. After the identification of a vehicle, it is tracked using color histogram similarity, applied in Windows centered on particles. The count of vehicles occurs in user-defined virtual loops on the tracks desired, through the intersection of convex figures traced with these virtual loops. Tests were performed using videos of six different scenes, totaling 81,000 frames. The results of vehicle counts obtained were compared to two current methods. A method has similar approach to proposed method (KIM, 2008), which attempts to establish groups of particles in ellipsoidal shapes. The other method (SÁNCHEZ et al., 2011) tracks connected objects, when these are different from the background, through the intersection of these objects between adjacent frames. Considering the total universe of vehicles examined, 1085, the results obtained by the proposed method showed an intermediate absolute difference in counting of vehicles to comparative methods, 53 against 66 and 27 vehicles for (KIM, 2008) and (SÁNCHEZ et al., 2011) respectively. The proposed method is the only one that counted vehicles less than the real value, while comparative methods counted vehicles beyond the real value. The proposed method loses 102 vehicles, lower than value to (SÁNCHEZ et al., 2011), 181, and roughly the same number as the method of (KIM, 2008), 101. The number of vehicles detected more than once are lower for the proposed method in relation to the comparative methods, 49 vehicles against 167 to (Kim, 2008) and 208 to (SANCHEZ et al., 2011). Processamento de sinais Rastreamento de veículos Processamento de imagens Computer vision Image processing Particles clustering Vehicle count Vehicle tracking Video processing Signal processing Instrumentation
32	Rastreamento veicular em vídeos de tráfego com aplicação em contagem de veículos Silva, Christiano Bouvié da January 2012 (has links) Esta dissertação apresenta o desenvolvido de um método, baseado em agrupamento de partículas, para realizar a contagem de veículos em vídeos de tráfego. Tal procedimento é importante em sistemas de tráfego inteligentes, ou como uma ferramenta auxiliar no planejamento de vias urbanas. Utilizando técnicas de processamento de imagens e agrupamento de partículas, o método proposto utiliza-se da coerência de movimento e posição espacial existente entre partículas extraídas das imagens de vídeo para agrupá-las, formando figuras convexas que são analisadas em busca de possíveis veículos. Essa análise leva em consideração a morfologia das figuras convexas e a informação de fundo da imagem, para unir ou dividir os agrupamentos. Após a identificação de um veículo, o mesmo é rastreado utilizando-se similaridade de histograma de cores, aplicado em janelas centradas nas partículas. A contagem dos veículos ocorre em laços virtuais definidos pelo usuário nas pistas desejadas, através da intersecção das figuras convexas rastreadas com estes laços virtuais. Testes foram realizados utilizando-se vídeos de seis cenas diferentes, totalizando 81.000 quadros. Os resultados das contagens de veículos obtidos foram comparados a dois métodos atuais. Um método possui abordagem similar ao método proposto (KIM, 2008), que tenta fixar agrupamentos de partículas em formas elipsoidais. O outro método (SÁNCHEZ et al., 2011) rastreia objetos conectados, quando estes são diferentes do fundo, através da intersecção destes objetos entre quadros adjacentes. Considerando-se o universo total de veículos analisados, 1085 veículos, os resultados obtidos pelo método proposto apresentaram uma diferença absoluta na contagem dos veículos intermediária aos métodos comparativos, 53 veículos contra 66 e 27 para (KIM, 2008) e (SÁNCHEZ et al., 2011) respectivamente, sendo o único método que contou menos veículos que o valor real, enquanto os métodos comparativos contaram veículos além do valor real. O método proposto perde 102 veículos, valor inferior ao método de (SÁNCHEZ et al., 2011), 181, e praticamente o mesmo número que o método de (KIM, 2008), 101. Já os veículos detectados mais de uma vez apresentam valores inferiores para o método proposto, 49, em relação aos métodos comparativos, 167 para (KIM, 2008) e 208 para (SÁNCHEZ et al., 2011). / This dissertation presents the developed of a method based on particle group, to conduct the count of vehicles in traffic videos. This procedure is important in intelligent traffic systems, or as an auxiliary tool in the planning of urban streets. Using image processing techniques and grouping of particles, the proposed method uses the coherence of spatial position and movement between particles extracted from the video footage to assemble them into convex figures that are parsed in search of possible vehicles. This analysis takes into account the morphology of convex figures and background information of the image, to merge or split the groups. After the identification of a vehicle, it is tracked using color histogram similarity, applied in Windows centered on particles. The count of vehicles occurs in user-defined virtual loops on the tracks desired, through the intersection of convex figures traced with these virtual loops. Tests were performed using videos of six different scenes, totaling 81,000 frames. The results of vehicle counts obtained were compared to two current methods. A method has similar approach to proposed method (KIM, 2008), which attempts to establish groups of particles in ellipsoidal shapes. The other method (SÁNCHEZ et al., 2011) tracks connected objects, when these are different from the background, through the intersection of these objects between adjacent frames. Considering the total universe of vehicles examined, 1085, the results obtained by the proposed method showed an intermediate absolute difference in counting of vehicles to comparative methods, 53 against 66 and 27 vehicles for (KIM, 2008) and (SÁNCHEZ et al., 2011) respectively. The proposed method is the only one that counted vehicles less than the real value, while comparative methods counted vehicles beyond the real value. The proposed method loses 102 vehicles, lower than value to (SÁNCHEZ et al., 2011), 181, and roughly the same number as the method of (KIM, 2008), 101. The number of vehicles detected more than once are lower for the proposed method in relation to the comparative methods, 49 vehicles against 167 to (Kim, 2008) and 208 to (SANCHEZ et al., 2011). Processamento de sinais Rastreamento de veículos Processamento de imagens Computer vision Image processing Particles clustering Vehicle count Vehicle tracking Video processing Signal processing Instrumentation
33	Increasing Autonomy of Unmanned Aircraft Systems Through the Use of Imaging Sensors Rudol, Piotr January 2011 (has links) The range of missions performed by Unmanned Aircraft Systems (UAS) has been steadily growing in the past decades thanks to continued development in several disciplines. The goal of increasing the autonomy of UAS's is widening the range of tasks which can be carried out without, or with minimal, external help. This thesis presents methods for increasing specific aspects of autonomy of UAS's operating both in outdoor and indoor environments where cameras are used as the primary sensors. First, a method for fusing color and thermal images for object detection, geolocation and tracking for UAS's operating primarily outdoors is presented. Specifically, a method for building saliency maps where human body locations are marked as points of interest is described. Such maps can be used in emergency situations to increase the situational awareness of first responders or a robotic system itself. Additionally, the same method is applied to the problem of vehicle tracking. A generated stream of geographical locations of tracked vehicles increases situational awareness by allowing for qualitative reasoning about, for example, vehicles overtaking, entering or leaving crossings. Second, two approaches to the UAS indoor localization problem in the absence of GPS-based positioning are presented. Both use cameras as the main sensors and enable autonomous indoor ight and navigation. The first approach takes advantage of cooperation with a ground robot to provide a UAS with its localization information. The second approach uses marker-based visual pose estimation where all computations are done onboard a small-scale aircraft which additionally increases its autonomy by not relying on external computational power. UAV UAS UAV autonomy human-body detection color-thermal image fusion vehicle tracking geolocation UAV indoor navigation
34	Use of improved Deep Learning and DeepSORT for Vehicle estimation / Användning av förbättrad djupinlärning och DeepSORT för fordonsuppskattning Zheng, Danna January 2022 (has links) Intelligent Traffic System (ITS) has high application value in nowadays vehicle surveillance and future applications such as automated driving. The crucial part of ITS is to detect and track vehicles in real-time video stream with high accuracy and low GPU consumption. In this project, we select the YOLO version4 (YOLOv4) one-stage deep learning detector to generate bounding boxes with vehicle classes and location as well as confidence value, we select Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) tracker to track vehicles using the output of YOLOv4 detector. Furthermore, in order to make the detector more adaptive to practical use, especially when the vehicle is small or obscured, we improved the detector’s structure by adding attention mechanisms and reducing parameters to detect vehicles with relatively high accuracy and low GPU memory usage. With the baseline model, results show that the YOLOv4 and DeepSORT vehicle detection could achieve 82.4% mean average precision among three vehicle classes with 63.945 MB parameters under 19.98 frames per second. After optimization, the improved model could achieve 85.84% mean average precision among three detection classes with 44.158MB parameters under 18.65 frames per second. Compared with original YOLOv4, the improved YOLOv4 detector could increase the mean average precision by 3.44% and largely reduced the parameters by 30.94% as well as maintaining high detection speed. This proves the validity and high applicability of the proposed improved YOLOv4 detector. / Intelligenta trafiksystem har ett stort tillämpningsvärde i dagens fordonsövervakning och framtida tillämpningar som t.ex. automatiserad körning. Den avgörande delen av systemet är att upptäcka och spåra fordon i videoströmmar i realtid med hög noggrannhet och låg GPU-förbrukning. I det här projektet väljer vi YOLOv4-detektorn för djupinlärning i ett steg för att generera avgränsande rutor med fordonsklasser och lokalisering samt konfidensvärde, och vi väljer DeepSORT-tracker för att spåra fordon med hjälp av YOLOv4-detektorns resultat. För att göra detektorn mer anpassningsbar för praktisk användning, särskilt när fordonet är litet eller dolt, förbättrade vi dessutom detektorns struktur genom att lägga till uppmärksamhetsmekanismer och minska parametrarna för att upptäcka fordon med relativt hög noggrannhet och låg GPU-minneanvändning. Med basmodellen visar resultaten att YOLOv4 och DeepSORT fordonsdetektering kunde uppnå en genomsnittlig genomsnittlig precision på 82.4 % bland tre fordonsklasser med 63.945 MB parametrar under 19.98 bilder per sekund. Efter optimering kunde den förbättrade modellen uppnå 85.84% genomsnittlig precision bland tre detektionsklasser med 44.158 MB parametrar under 18.65 bilder per sekund. Jämfört med den ursprungliga YOLOv4-detektorn kunde den förbättrade YOLOv4-detektorn öka den genomsnittliga precisionen med 3.44 % och minska parametrarna med 30.94%, samtidigt som den bibehöll en hög detektionshastighet. Detta visar att den föreslagna förbättrade YOLOv4-detektorn är giltig och mycket användbar. YOLOv4 algorithm deep learning DeepSORT vehicle detection attention mechanism vehicle tracking YOLOv4-algoritmen djupinlärning DeepSORT fordonsdetektering fordonsspårning Computer and Information Sciences Data- och informationsvetenskap
35	Měření rychlosti vozidel pomocí stereo kamery / Vehicle Speed Measurement Using Stereo Camera Pair Najman, Pavel January 2021 (has links) Tato práce se snaží najít odpověď na otázku, zda je v současnosti možné autonomně měřit rychlost vozidel pomocí stereoskopické měřící metody s průměrnou chybou v rozmezí 1 km/h, maximální chybou v rozmezí 3 km/h a směrodatnou odchylkou v rozmezí 1 km/h. Tyto rozsahy chyb jsou založené na požadavcích organizace OIML, jejichž doporučení jsou základem metrologických legislativ mnoha zemí. Pro zodpovězení této otázky je zformulována hypotéza, která je následně testována. Metoda, která využívá stereo kameru pro měření rychlosti vozidel je navržena a experimentálně vyhodnocena. Výsledky pokusů ukazují, že navržená metoda překonává výsledky dosavadních metod. Průměrná chyba měření je přibližně 0.05 km/h, směrodatná odchylka chyby je menší než 0.20 km/h a maximální absolutní hodnota chyby je menší než 0.75 km/h. Tyto výsledky jsou v požadovaném rozmezí a potvrzují tedy testovanou hypotézu.
36	Analýza pohybu automobilů na křižovatkách / Movement Analysis of Vehicles on Crossroads Benček, Vladimír January 2016 (has links) This thesis proposes and implements a system for movement analysis of vehicles on crossroads. It detects and tracks the movement of vehicles in the video, gained from the stationary video camera, which has the view of some crossroad. The trajectories are stored and their number and directions are analysed. The detection was made using cascade classifier. A dataset of 10500 positive and 10500 negative samples has been created to train the classifier. Vehicles are tracked using KCF method. For trajectory clustering, needed by analysis, the Mean Shift method is used. Testing showed, that the overall success of vehicle movement analysis is 92.77%.
37	The value of vehicle tracking technology in the recovery of stolen motor vehicles Senekal, Willem Andries 03 1900 (has links) Text in English / In this study, the research problem sought to explore, identify and acknowledge the value of vehicle tracking technology within the South African Police Service (SAPS). National legislation in the Republic of South Africa allows the SAPS and private organisations, such as Tracker, to create partnerships to successfully combat crime, such as vehicle related crimes. Data was collected by means of a literature study, together with semi-structured interviews that were individually conducted with non-commissioned officers of the SAPS: West Rand Flying Squad. These members are deployed daily, in an operational environment, to deal with the recovery of stolen and robbed motor vehicles; they utilise vehicle tracking technology to fulfil this function. A detailed study of literature relating to national legislation, SAPS directives, media and newspaper reports as well as library resources and international studies was conducted. The research indicates the importance of vehicle tracking technology in assisting specialized units within the SAPS to successfully and efficiently track and locate stolen or robbed motor vehicles. It is evident that the use of this type of technology has become an invaluable tool to the SAPS: West Rand Flying Squad members in their daily duties. Furthermore, members at grassroots level understand and appreciate the assistance and value of technology, especially as the technology enables them to effectively recover stolen or robbed motor vehicles, and to successfully arrest the perpetrators responsible for these thefts. The recommendations made in this study may provide a number of solutions to the South African government, SAPS, insurance industry and the general public, regarding the value of vehicle tracking technology. In addition, the study indicates how this technology can effectively assist in curbing vehicle crimes and the recovery of stolen or robbed motor vehicles; in the process, recovery affects the arrests of criminals, thus saving the economy a significant amount of money due to crimes of this nature. / Police Practice / M.A. (Criminal Justice) Vehicle theft Vehicle robbery Vehicle tracking Vehicle tracking technology Stolen motor vehicles Robbed motor vehicles Recovery of stolen motor vehicles Recovery of robbed motor vehicles Tracker 363.259628629040968222 South African Police Service Global Positioning System Automatic data collection systems
38	Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms Vestin, Albin, Strandberg, Gustav January 2019 (has links) Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms. evaluation target tracking multiple sensors non-causal smoother smoothing tracking vehicle tracking camera lidar estimate estimation prediction vehicle dynamics sensor fusion real-time tracking extended kalman filter filter validation validation position estimation velocity estimation dynamic model model complexity multi object tracking multiple object tracking single object tracking data association tracking fundamentals iterated kalman filter track management gnn global nearest neighbour mahalanobis mahalanobis distance performance evaluation differential gps dgps roi ego several sensors sensors rmse root mean square error invertible motion anti-causal motion anti-causal tracking constant velocity gnn imu tfs two filter smoother ekf rts radar inertial measurement unit nonlinear nonlinear systems mono camera monocular camera noise model tracking performance fixed interval smoothing m/n logic centralized fusion non-causal object tracker car tracking car dynamics automotive active safety object tracking automotive industry thesis master reverse dynamics reverse tracking reverse sequence sequence tracking data propagation ground truth estimating ground truth additional sensors mounted sensors true estimates environment comparison algorithm independent targets overlapping measurements occluded track switch improve lower uncertainty more certain state process noise covariance sampling image sprt adas cnn cv pdf track target ego tracker tentative track observatiom online tracking offline tracking online offline recorded sequences robust self driving self-driving car traffic trajectory true state scenario scenarios future accurate output advanced driver assistance systems non-linear complex noise pedestrian truck bus maneuvering vehicles processed measurement frame state correction probability density function tuning likelihood transition measurement motion model recursion gaussian approximation distribution linear jacobian multiplicative noise ratio ad hoc ad hoc state space approach backward auction euclidean distance statistical threshold gating association margin normalize covariance matrix fusion confirmed rejected tentative history absolute error modular ego motion parameters variables logg hardware specification fused causal factorization independent uncorrelated transform moving rotation translation oncoming overtaking Control Engineering Reglerteknik

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