This thesis deals with segmentation of tennis player´s body parts for analysis its motion. Testing code was written in C++ with use of OpenCV library. Some image processing techniques such as thresholding, background subtraction or finding the largest contours were implemented. There was used a linear triangulation technique for calculating the 3D coordinates of segmented points.

Traffic monitoring using computer vision is becoming the desired system in practice. It allows nondestructive installation and also is very useful in many applications. This thesis focuses on automatic detection of vehicles approaching to a crossroads. This work also includes description of selected methods for detecting moving vehicles and the way of tracking them. On the basis of these methods is designed application that is implemented and tested in different lighting and weather conditions and various direction of approaching vehicles.

Context : Air being the free source is used in different ways commercially. In earlier days windmills generate power, water, and electricity. The excessive establishment of windmills for commercial purposes affected avifauna. Most of the birds lost their lives due to collisions with windmills. Turbines used to generate power near airports are also one of the causes for the extinction of birdlife. According to a survey in 2011 in Canada a total of 23,300 bird deaths were caused by wind turbines and also it is estimated that the number of deaths would increase to 2,33,000 in the following 10-15 years. Objectives : The main objective of this thesis is to find a suitable software solution to detect the birds on a series of grayscale images in real-time and in minimum full HD resolution with at least a 15 FPS rate. User-Driven Design Methodology is used for developing, tools are Python and Open-CV. Methods : In this research, a system is designed to detect the bird in an HD Video. Possible methods that can be considered are convolutional neural networks (CNN), vision based detection with background subtraction, contour detection and confusion matrix classification. These methods detect birds in raw images and with help of a classifier make it possible to see the bird in desired pixels with full resolution. We will investigate a bird classification method consisting of two steps, background subtraction, and then object classification. Background subtraction is a fundamental method to extract moving objects from a fixed background. For classification, we will use the YOLO v3 model version for object classification. Results : The project is expected to result in a system design and prototype for the bird identification on a grayscale video stream in at least full HD resolution in a minimum of 15 FPS. The bird should be distinguished from other moving objects like wind turbine blades, trees, or clouds. The proposed solution should identify up to 5 birds simultaneously. Conclusion : After completing each step and arriving at the classification, the methods we have tried, such as Haar Cascades and mobile-net SSD, were not providing us with the desired results. So we opted to use YOLO v3, which had the best accuracy in classifying different objects. By using the YOLO v3 classifier, we have detected the bird with 95% accuracy, blades with 90% accuracy, clouds with 80% accuracy, trees with 70% accuracy. Moreover, we conclude that there is a need for further empirical validation of the models in full-scale industry trials.

The use of external dimensions in products has great importance in numerous areas and a digital measurement can lead to a more streamlined workflow. In this project are the suitability of sensors for measuring irregularly shaped objects investigated and a prototype for digital measurement built. The prototype consists of a measuring cart, Raspberry Pi, two cameras and MQTT with React web front-end. Measurement is started by a client and reported back to the client with the measurement station’s established measurement values. The result is presented as 79% of measured dimensions ending up within ±10 mm margin of the manually measured value. The result is based on tests of 10 objects with deviating characteristics to challenge the measurement capacity. The biggest challenge that has arisen has been the handling of objects’ perspectives in relation to the cameras. It is one of the areas that could be further developed to improve the reliability of the measuring station. / Användningen av yttermått hos produkter har en stor betydelse inom en stor mängd områden och en digital mätning av detta kan medföra en mer strömlinjeformad arbetsgång. I detta projektet undersöks sensorers lämplighet för mätning av oregelbundet formade objekt och det byggs en prototyp för digital mätning. Prototypen består av en mätvagn, Raspberry Pi, två kameror och MQTT med React-baserat webbgränssnitt. Mätning startas av en klient och rapporteras tillbaka till klienten med mätstationens fastslagna mätvärdena. Resultatet slutar i att 79% av uppmätta dimensioner hamnar inom ±10 mm marginal av manuellt uppmätt värde. Ett resultat som baseras på tester av 10 objekt med avvikande egenskaper för att utmana mätkapaciteten. Det största utmaningen som uppkommit har varit hanteringen av objekts perspektiv i förhållande till kamerorna. Det är ett av områdena som fortsatt har möjlighet att vidarutvecklas för att ytterliggare förbättra mätstationens pålitlighet.

Abstract Gestures are present in most daily human activities and automatic gestures analysis is a significant topic with the goal of enabling the interaction between humans and computers as natural as the communication between humans. From a computer vision perspective, a gesture analysis system is typically composed of two stages, the low-level stage for human motion detection and the high-level stage for understanding human gestures. Therefore, this thesis contributes to the research on gesture analysis from two aspects, 1) Detection: human motion segmentation from video sequences, and 2) Understanding: gesture cues extraction and recognition. In the first part of this thesis, two sparse signal recovery based human motion detection methods are presented. In real videos the foreground (human motions) pixels are often not randomly distributed but have the group properties in both spatial and temporal domains. Based on this observation, a spatio-temporal group sparsity recovery model is proposed, which explicitly consider the foreground pixels' group clustering priors of spatial coherence and temporal contiguity. Moreover, a pixel should be considered as a multi-channel signal. Namely, if a pixel is equal to the adjacent ones that means all the three RGB coefficients should be equal. Motivated by this observation, a multi-channel fused Lasso regularizer is developed to explore the smoothness of multi-channels signals. In the second part of this thesis, two human gesture recognition methods are presented to resolve the issue of temporal dynamics, which is crucial to the interpretation of the observed gestures. In the first study, a gesture skeletal sequence is characterized by a trajectory on a Riemannian manifold. Then, a time-warping invariant metric on the Riemannian manifold is proposed. Furthermore, a sparse coding for skeletal trajectories is presented by explicitly considering the labelling information, with the aim to enforcing the discriminant validity of the dictionary. In the second work, based on the observation that a gesture is a time series with distinctly defined phases, a low-rank matrix decomposition model is proposed to build temporal compositions of gestures. In this way, a more appropriate alignment of hidden states for a hidden Markov model can be achieved. / Tiivistelmä Eleet ovat läsnä useimmissa päivittäisissä ihmisen toiminnoissa. Automaattista eleiden analyysia tarvitaan laitteiden ja ihmisten välisestä vuorovaikutuksesta parantamiseksi ja tavoitteena on yhtä luonnollinen vuorovaikutus kuin ihmisten välinen vuorovaikutus. Konenäön näkökulmasta eleiden analyysijärjestelmä koostuu ihmisen liikkeiden havainnoinnista ja eleiden tunnistamisesta. Tämä väitöskirjatyö edistää eleanalyysin-tutkimusta erityisesti kahdesta näkökulmasta: 1) Havainnointi - ihmisen liikkeiden segmentointi videosekvenssistä. 2) Ymmärtäminen - elemarkkerien erottaminen ja tunnistaminen. Väitöskirjan ensimmäinen osa esittelee kaksi liikkeen havainnointi menetelmää, jotka perustuvat harvan signaalin rekonstruktioon. Videokuvan etualan (ihmisen liikkeet) pikselit eivät yleensä ole satunnaisesti jakautuneita vaan niillä toisistaan riippuvia ominaisuuksia spatiaali- ja aikatasolla tarkasteltuna. Tähän havaintoon perustuen esitellään spatiaalis-ajallinen harva rekonstruktiomalli, joka käsittää etualan pikseleiden klusteroinnin spatiaalisen koherenssin ja ajallisen jatkuvuuden perusteella. Lisäksi tehdään oletus, että pikseli on monikanavainen signaali (RGB-väriarvot). Pikselin ollessa samankaltainen vieruspikseliensä kanssa myös niiden värikanava-arvot ovat samankaltaisia. Havaintoon nojautuen kehitettiin kanavat yhdistävä lasso-regularisointi, joka mahdollistaa monikanavaisen signaalin tasaisuuden tutkimisen. Väitöskirjan toisessa osassa esitellään kaksi menetelmää ihmisen eleiden tunnistamiseksi. Menetelmiä voidaan käyttää eleiden ajallisen dynamiikan ongelmien (eleiden nopeuden vaihtelu) ratkaisemiseksi, mikä on ensiarvoisen tärkeää havainnoitujen eleiden oikein tulkitsemiseksi. Ensimmäisessä menetelmässä ele kuvataan luurankomallin liikeratana Riemannin monistossa (Riemannian manifold), joka hyödyntää aikavääristymille sietoista metriikkaa. Lisäksi esitellään harvakoodaus (sparse coding) luurankomallien liikeradoille. Harvakoodaus perustuu nimiöintitietoon, jonka tavoitteena on varmistua koodisanaston keskinäisestä riippumattomuudesta. Toisen menetelmän lähtökohtana on havainto, että ele on ajallinen sarja selkeästi määriteltäviä vaiheita. Vaiheiden yhdistämiseen ehdotetaan matala-asteista matriisihajotelmamallia, jotta piilotilat voidaan sovittaa paremmin Markovin piilomalliin (Hidden Markov Model).

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Surveillance has become, in the last years, something ubiquity in our society. Every day it is more notorious the presence of intelligent systems for surveillance in our everyday life. This is due to technological advances achieved in recent decades (storage and processing speed increasing, miniaturization of devices like biometric detectors and video cameras) as the constant feeling of insecurity experienced in several countries. Following the dark days of 9/11, security and surveillance became paramount. This work aims the study of techniques for the development of a surveillance system of an outdoor parking lot based on a stationary camera. Considering that in an outdoor parking lot it is very important that the surveillance is made both day and night, in this work we use an infrared camera to record images. An infrared camera allows to see objects of interest in the scene even at night. The images used for the experiments in this work were recorded by the student in Laval University campus (Canada) during an internship he held in the "Canada Research Chair in Multipolar Infrared Vision". A surveillance system based on video cameras is usually composed of three parts: (i) motion detection, (ii) tracking and (iii)event management. In this work, we use a dynamic background subtraction technique to detect motion (motion segmentation). This technique adapts to abrupt changes on the scene's illumination making the technique robust to this changes. Besides, we use ow analysis to restrict the segmentation process only to regions where we have motion in the scene. The object tracking technique used is based on a two phase cycle: prediction and correction. The events of interest which occur in the monitored area are modeled explicitly and then recognized and interpreted. The main goal of this project is to recognize suspicious events. Experimental results show that such techniques are suitable for a surveillance system for an outdoor parking lot based on a infrared stationary camera. / Vigilância se tornou, nos últimos anos, algo ubíquo em nossa sociedade. Cada dia que passa é mais notória a presença de sistemas inteligentes de vigilância em nosso dia-a-dia. Isso se deve tanto aos avanços tecnológicos alcançados nas últimas décadas (aumento da capacidade de processamento e armazenamento, miniaturização de dispositivos como detectores biométricos e câmeras de vídeo) como a constante sensação de insegurança vivida em vários países. Após os dias sombrios de 11/09, segurança e vigilância se tornaram algo primordial. Este trabalho visa o estudo de técnicas para o desenvolvimento de um sistema de vigilância para um estacionamento externo baseado em uma câmera estacionária. Tendo em vista que em um estacionamento externo é de suma importância que a vigilância seja feita tanto de dia quanto de noite, neste trabalho utilizamos uma câmera que captura imagens infravermelhas. Uma câmera infravermelha permite que enxerguemos objetos de interesse na cena até mesmo a noite. As imagens usadas nos experimentos realizados neste trabalho foram colhidas no campus da Universidade de Laval (Canadá) durante um estágio realizado no Canada Research Chair in Multipolar Infrared Vision. Um sistema de vigilância baseado em câmeras de vídeo geralmente possui três partes principais: (i) detecção de movimento, (ii) monitoramento e (iii) gerenciamento de eventos. Neste trabalho, utilizamos uma dinâmica técnica de subtração de plano de fundo para realizar a detecção de movimento (segmentação de movimento). Esta técnica se adapta às mudanças bruscas de iluminação na cena tornando o método de segmentação robusto a estas mudanças. Além disso, utilizamos análise de uxo de movimento para restringir a segmentação somente às regiões onde existem algum movimento na cena. A técnica de monitoramento de objetos em movimento usada neste trabalho é baseada em um ciclo de dois estágios: previsão e correção. Os eventos de interesse que ocorrem na área monitorada são modelados de forma explícita sendo então reconhecidos e interpretados. O foco principal deste trabalho é o reconhecimento de eventos suspeitos. Resultados experimentais obtidos mostram que tais técnicas são adequadas para um sistema de vigilância de um estacionamento externo baseado em uma câmera estacionária infravermelha. / Mestre em Ciência da Computação

L’intralogistique (ou logistique interne) s’intéresse au traitement et à l’optimisation des flux physiques au sein des entrepôts, centres de distribution et usines. Les systèmes de manutention automatisés sont au cœur de la logistique interne de plusieurs industries comme le commerce en ligne, la messagerie postale, la grande distribution, l’industrie manufacturière, le transport aéroportuaire, etc. Ces équipements composés de lignes de convoyage haute cadence permettent un transport sûr et fiable d’un volume considérable de biens et de marchandises tout en réduisant les coûts.L’automatisation de l’acheminement des flux physiques par les systèmes de manutention repose sur l’identification et le suivi en temps réel des charges transportées. Dans cette thèse, nous explorons une solution de suivi qui emploie un réseau de caméras intelligentes à champs recouvrants. L’objectif final étant de fournir l’information de suivi sur les charges transportées pour le pilotage d’un système de manutention.Le suivi d’objets est un problème fondamental de la vision par ordinateur qui a de nombreuses applications comme la vidéosurveillance, la robotique, les voitures autonomes, etc. Nous avons intégré plusieurs briques de base issues de la vidéosurveillance et traditionnellement appliquées aux scènes de surveillance automobile ou de surveillance des activités humaines pour constituer une chaine de suivi de référence. Cette chaine d’analyse vidéo étalon nous a permis de caractériser des hypothèses propres au convoyage d’objet. Nous proposons dans cette thèse d’incorporer cette connaissance métier dans la chaine de suivi pour en améliorer les performances. Nous avons, notamment pris en compte, dans l’étape de segmentation des images, le fait que les objets doivent pouvoir s’arrêter sans pour autant être intégrés aux modèles d’arrière-plan. Nous avons également exploité la régularité des trajectoires des objets convoyés dans les installations, permettant d’améliorer les modèles prédictifs de la position et de la vitesse des objets, dans les étapes de suivi. Enfin, nous avons intégré des contraintes de stricte monotonie dans l’ordre des colis sur le convoyeur, contraintes qui n’existent pas dans les scènes généralistes, pour ré-identifier les objets dans les situations où ils sont proches des eux les autres.Nous nous sommes par ailleurs attelés à un problème pratique d’optimisation des performances sur l’architecture multi-cœurs couplée aux caméras intelligentes. Dans ce cadre, nous avons a mis en place un apprentissage dynamique de la zone de l’image contenant le convoyeur. Cette zone d’intérêt nous a permis de limiter la mise à jour du modèle de fond à cette seule zone. Nous avons, par la suite, proposé une stratégie de parallélisation qui partitionne de manière adaptative cette région d’intérêt de l’image, afin d’équilibrer au mieux la charge de travail entre les différents cœurs de l’architecture des caméras intelligentes.Nous avons également traité la problématique du suivi sur plusieurs caméras. Nous avons proposé une approche basée sur un système de composition d’évènements. Cette approche nous a permis de fusionner les données de suivi local pour former les trajectoires globales des colis, tout en intégrant des informations issues du processus métier, par exemple la saisie de l’information de destination par des opérateurs sur un terminal avant la dépose des colis. Nous avons validé cette approche sur un système de manutention mis en place dans un centre de tri postal de grande envergure. Le réseau de caméras déployé est composé de 32 caméras qui assurent le suivi de plus de 400.000 colis/jour sur des lignes de dépose. Le taux d’erreur du suivi obtenu est inférieur à 1 colis sur 1000 (0,1%). / Intralogistics (or internal logistics) focuses on the management and optimization of internal production and distribution processes within warehouses, distribution centers, and factories. Automated handling systems play a crucial role in the internal logistics of several industries such as e-commerce, postal messaging, retail, manufacturing, airport transport, etc. These systems are composed by multiple high-speed conveyor lines that provide safe and reliable transportation of a large volume of goods and merchandise while reducing costs.The automation of the conveying process relies on the identification and the real-time tracking of the transported loads. In this thesis, we designed a tracking solution that employs a network of smart cameras with an overlapping field of view. The goal is to provide tracking information to control an automated handling system.Multiple object tracking is a fundamental problem of computer vision that has many applications such as video surveillance, robotics, autonomous cars, etc. We integrated several building blocks traditionally applied to traffic surveillance or human activities monitoring to constitute a tracking pipeline. We used this baseline tracking pipeline to characterize contextual scene information proper to the conveying scenario. We integrated this contextual information to the tracking pipeline to enhance the performance. In particular, we took into account the state of moving objects that become stationary in the background subtraction step to prevent their absorption to the background model. We have also exploited the regularity of objects trajectory to enhance the motion model associated with the tracked objects. Finally, we integrated the precedence ordering constraint among the conveyed object to reidentify them when they are close to each other.We have also tackled practical problems related to the optimization the execution of the proposed tracking problem in the multi-core architectures of smart cameras. In particular, we proposed a dynamic learning process that extracts the region of the image that corresponds to the conveyor lines. We reduced the number of the processed pixel by restricting the processing to this region of interest. We also proposed a parallelization strategy that adaptively partitions this region of interest of the image, in order to balance the workload between the different cores of the smart cameras.Finally, we proposed a multiple cameras tracking algorithms based on event composition. This approach fuses the local tracking generated by the smart cameras to form global object trajectories and information from third party systems such as the destination of the object entered by operators on a terminal. We validated the proposed approach for the control of a sorting system deployed in a postal distribution warehouse. A network of cameras composed of 32 cameras tracks more than 400.000 parcel/day in injections lines. The tracking error rate is less than 1 parcel in a 1000 (0.1%).

For 500 years transient light phenomena (TLP) have been observed on the lunar surface by ground-based observers. The actual physical reason for most of these events is today still unknown. Current plans of NASA and SpaceX to send astronauts back to the Moon and already successful deep-space CubeSat mission will allow in the future research nanosatellite missions to the cislunar space. This thesis presents a new hardware and software concept for a future payload on such a nanosatellite. The main task was to develop and implement a high-performance image processing algorithm which task is to detect short brightening flashes on the lunar surface. Based on a review of historic reported phenomena, possible explanation theories for these phenomena and currently active and planed ground- or space-based observatories possible reference scenarios were analyzed. From the presented scenarios one, the detection of brightening events was chosen and requirements for this scenario stated. Afterwards, possible detectors, processing computers and image processing algorithms were researched and compared regarding the specified requirements. This analysis of available algorithm was used to develop a new high-performance detection algorithm to detect transient brightening events on the Moon. The implementation of this algorithm running on the processor and the internal GPU of a MacMini achieved a framerate of 55 FPS by processing images with a resolution of 4.2 megapixel. Its functionality and performance was verified on the remote telescope operated by the Chair of Space Technology of the University of Würzburg. Furthermore, the developed algorithm was also successfully ported on the Nvidia Jetson Nano and its performance compared with a FPGA based image processing algorithm. The results were used to chose a FPGA as the main processing computer of the payload. This concept uses two backside illuminated CMOS image sensor connected to a single FPGA. On the FPGA the developed image processing algorithm should be implemented. Further work is required to realize the proposed concept in building the actual hardware and porting the developed algorithm onto this platform.

Thesis deals with the detection and tracking of small moving objects from static images. This work shows a general overview of methods and approaches to detection and tracking of objects. There are also described some other approaches to the whole solution. It also included basic definitions, such a noise, convolution and mathematical morphology. The work described Bayesian filtering and Kalman filter. It described the theory of Wavelets, wavelets filters and transformations. The work deals with different ways of the blob`s detection. It is here the design and implementation of applications, which is based on the wavelets filters and Kalman filter. It`s implemented several methods of background subtraction, which are compared by testing. Testing and application are designed to detect vehicles, which are moving faraway (at least 200 m away).

The effective surveillance of large critical areas is crucial for their security and privacy. There is no publicly available and acceptable solution of automating this task. This thesis aims to create an application utilizing a combination of a pan-tilt robotic manipulator and a visible-spectrum camera. Based on the pan-tilt unit's position and camera's images, the application searches for semantically significant changes in the captured environment and marks these regions of interest.