In this thesis, we develop an algorithm for detecting independent motion in real-time from 2D image sequences of arbitrarily complex 3D scenes. We discuss the necessary background information in image formation, optical flow, multiple view geometry, robust estimation, and real-time camera and scene pose estimation for constructing and understanding the operation of our algorithm. Furthermore, we provide an overview of existing independent motion detection techniques and compare them to our proposed solution. Unfortunately, the existing independent motion detection techniques were not evaluated quantitatively nor were their source code made publicly available. Therefore, it is not possible to make direct comparisons. Instead, we constructed several comparison algorithms which should have comparable performance to these previous approaches. We developed methods for quantitatively comparing independent motion detection algorithms and found that our solution had the best performance. By establishing a method for quantitatively evaluating these algorithms and publishing our results, we hope to foster better research in this area and help future investigators more quickly advance the state of the art. / text

Στην παρούσα διπλωματική εργασία γίνεται μελέτη και ανάλυση της ανθρώπινης κίνησης με σκοπό την αναγνώριση και τον χαρακτηρισμό της. Στο κεφάλαιο 1 παρουσιάζεται το θεωρητικό υπόβαθρο, περιγράφονται εν συντομία τα μέρη της ανάλυσης μιας ολοκληρωμένης διαδικασίας και ορίζονται οι έννοιες οι οποίες θα χρησιμοποιηθούν στην συνέχεια. Στο κεφάλαιο 2 παρουσιάζονται τα μοντέλα και οι τεχνικές που χρησιμοποιούνται κυρίως για την αφαίρεση φόντου σε μια εικόνα και γίνεται υλοποίηση και εφαρμογή, ορισμένων από αυτών, σε βίντεο με συγκεκριμένα χαρακτηριστικά με στόχο την σύγκριση των αποτελεσμάτων. Στο κεφάλαιο 3 παρουσιάζονται οι τεχνικές, και οι κύριοι αντιπρόσωποι αυτών, που χρησιμοποιούνται για την αναγνώριση κινούμενης οντότητας εντός μιας ακολουθίας εικόνων. Στο κεφάλαιο 4 γίνεται υλοποίηση αλγόριθμων, σύμφωνα με τις τεχνικές που αναπτύχτηκαν στο κεφάλαιο 3, και εφαρμογής τους σε βίντεο ώστε να μελετήσουμε τα αποτελέσματα, επίσης παρουσιάζονται οι δυνατότητες του simulink και πως μπορούμε να το χρησιμοποιήσουμε ως εργαλείο για να πετύχουμε ίδια αποτελέσματα με αυτά από την συγγραφή κώδικα σε matlab. Στο τελευταίο κεφάλαιο παρουσιάζονται οι τεχνικές που έχουν χρησιμοποιηθεί στην διεθνή βιβλιογραφία για την αναγνώριση κίνησης και στην συνέχεια γίνεται ανάπτυξη αλγόριθμου που χρησιμοποιεί ως αναγνωριστικό χαρακτηριστικό το κέντρο μάζας της κινούμενης οντότητας και μέσω αυτού προσδιορίζεται η μορφή της κίνησης. / n this thesis study and analysis of human motion for the recognition and characterization of. Chapter 1 presents the theoretical background, outlines the parts of analysis of an integrated process and defines the concepts that will used then. Chapter 2 presents the models and techniques are mainly used to remove a background image and is implementation and enforcement, some of them, in video certain characteristics in order to compare the results. At Chapter 3 presents the techniques, and the main representatives of those who used to identify an entity within a moving sequence of images. Chapter 4 is implementing algorithms under the techniques being developed in Chapter 3, and their application to video To study the results also shows the potential of simulink and how we can use it as a tool to achieve same results with the ones writing code in matlab. In the last chapter presents the techniques used in international literature to identify traffic and then becomes growth algorithm used as an identifier attribute the center of mass the moving entity and this is determined by the shape of motion.

Thesis (MSc (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2008. / In this paper, tracking tools are developed based on object features to robustly track the object using particle filtering. Automatic on-line initialisation techniques use motion detection and dynamic background modelling to extract features of moving objects. Automatically adapting the feature models during tracking is implemented and tested.

Algoritmos de detecção de movimento em seqüências de imagens devem satisfazer os requisitos de precisão, robustez e velocidade de processamento. A forma de combinar estes três itens depende do desenvolvimento do algoritmo e da aplicação a que se destina, sem que os itens de robustez e precisão sejam comprometidos. Neste trabalho investigamos técnicas para detecção do movimento que satisfazem tais requisitos. A técnica escolhida para detecção de movimento foi a do fluxo Ótico (FO) devido as suas características de precisão nos resultados. Como esta técnica exige elevado esforço computacional, propõe-se nesta tese uma metodologia que aplica as equações de fluxo ótico em reduzidas áreas da imagem processada. Estas áreas são selecionadas utilizando algumas técnicas de pré-processamento que identificam regiões da imagem com maior probabilidade de movimento presente. Posteriormente a esta identificação são aplicadas as equações de FO nas regiões de interesse. Para avaliação e validação do método proposto, comparam-se os diagramas de agulhas resultantes das áreas reduzidas aos diagramas resultantes de toda a imagem mediante critérios estatísticos e de tempo de processamento envolvido. Os algoritmos são testados utilizando imagens sintéticas e imagens reais. / Algorithms for motion detection in image sequences must satisfy the following requirements: accuracy, robustness and speed. The way that accuracy, robustness and speed are combined depends on the algorithm development and on the application. In this work, it has investigated motion detection techniques that satisfy the mentioned requirements. The Optical Flow technique was chosen for the motion detection due to its good performance in the results. As the Optical Flow requires intensive computational load, we propose in this thesis a methodology where Optical Flow Equations are applied in specific areas of the processed image. These areas were selected using pre-processing techniques that identify regions of image with larger motion probability. After the motion areas identification, Optical Flow Equations are applied to the regions of interest. To assess and validate the proposed method, the needle diagrams obtained in the reduced areas are compared with the ones obtained from the whole image according to statistical criteria and processing time. The proposed algorithms have been tested in synthetic and real images.

La détection de mouvement est une opération de base souvent utilisée en vision par ordinateur, que ce soit pour la détection de piétons, la détection d’anomalies, l’analyse de scènes vidéo ou le suivi d’objets en temps réel. Bien qu’un très grand nombre d’articles ait été publiés sur le sujet, plusieurs questions restent en suspens. Par exemple, il n’est toujours pas clair comment détecter des objets en mouvement dans des vidéos contenant des situations difficiles à gérer comme d'importants mouvements de fonds et des changements d’illumination. De plus, il n’y a pas de consensus sur comment quantifier les performances des méthodes de détection de mouvement. Aussi, il est souvent difficile d’incorporer de l’information de mouvement à des opérations de haut niveau comme par exemple la détection de piétons. Dans cette thèse, j’aborde quatre problèmes en lien avec la détection de mouvement: 1. Comment évaluer efficacement des méthodes de détection de mouvement? Pour répondre à cette question, nous avons mis sur pied une procédure d’évaluation de telles méthodes. Cela a mené à la création de la plus grosse base de données 100\% annotée au monde dédiée à la détection de mouvement et organisé une compétition internationale (CVPR 2014). J’ai également exploré différentes métriques d’évaluation ainsi que des stratégies de combinaison de méthodes de détection de mouvement. 2. L’annotation manuelle de chaque objet en mouvement dans un grand nombre de vidéos est un immense défi lors de la création d’une base de données d’analyse vidéo. Bien qu’il existe des méthodes de segmentation automatiques et semi-automatiques, ces dernières ne sont jamais assez précises pour produire des résultats de type “vérité terrain”. Pour résoudre ce problème, nous avons proposé une méthode interactive de segmentation d’objets en mouvement basée sur l’apprentissage profond. Les résultats obtenus sont aussi précis que ceux obtenus par un être humain tout en étant 40 fois plus rapide. 3. Les méthodes de détection de piétons sont très souvent utilisées en analyse de la vidéo. Malheureusement, elles souffrent parfois d’un grand nombre de faux positifs ou de faux négatifs tout dépendant de l’ajustement des paramètres de la méthode. Dans le but d’augmenter les performances des méthodes de détection de piétons, nous avons proposé un filtre non linéaire basée sur la détection de mouvement permettant de grandement réduire le nombre de faux positifs. 4. L’initialisation de fond ({\em background initialization}) est le processus par lequel on cherche à retrouver l’image de fond d’une vidéo sans les objets en mouvement. Bien qu’un grand nombre de méthodes ait été proposé, tout comme la détection de mouvement, il n’existe aucune base de donnée ni procédure d’évaluation pour de telles méthodes. Nous avons donc mis sur pied la plus grosse base de données au monde pour ce type d’applications et avons organisé une compétition internationale (ICPR 2016). / Abstract : Motion detection is a basic video analytic operation on which many high-level computer vision tasks are built upon, e.g., pedestrian detection, anomaly detection, scene understanding and object tracking strategies. Even though a large number of motion detection methods have been proposed in the last decades, some important questions are still unanswered, including: (1) how to separate the foreground from the background accurately even under extremely challenging circumstances? (2) how to evaluate different motion detection methods? And (3) how to use motion information extracted by motion detection to help improving high-level computer vision tasks? In this thesis, we address four problems related to motion detection: 1. How can we benchmark (and on which videos) motion detection method? Current datasets are either too small with a limited number of scenarios, or only provide bounding box ground truth that indicates the rough location of foreground objects. As a solution, we built the largest and most objective motion detection dataset in the world with pixel accurate ground truth to evaluate and compare motion detection methods. We also explore various evaluation metrics as well as different combination strategies. 2. Providing pixel accurate ground truth is a huge challenge when building a motion detection dataset. While automatic labeling methods suffer from a too large false detection rate to be used as ground truth, manual labeling of hundreds of thousands of frames is extremely time consuming. To solve this problem, we proposed an interactive deep learning method for segmenting moving objects from videos. The proposed method can reach human-level accuracies while lowering the labeling time by a factor of 40. 3. Pedestrian detectors always suffer from either false positive detections or false negative detections all depending on the parameter tuning. Unfortunately, manual adjustment of parameters for a large number of videos is not feasible in practice. In order to make pedestrian detectors more robust on a large variety of videos, we combined motion detection with various state-of-the-art pedestrian detectors. This is done by a novel motion-based nonlinear filtering process which improves detectors by a significant margin. 4. Scene background initialization is the process by which a method tries to recover the RGB background image of a video without foreground objects in it. However, one of the reasons that background modeling is challenging is that there is no good dataset and benchmarking framework to estimate the performance of background modeling methods. To fix this problem, we proposed an extensive survey as well as a novel benchmarking framework for scene background initialization.

Autonomous robot navigation in dynamic environments requires robust detection of egomotion and independent motion. This thesis introduces a novel solution to the problem of visual independent motion detection by interpreting the topological features of omnidirectional dense optical flow field and determining the background - egomotion direction. The thesis solves the problem of visual independent motion detection in four interdependent subtasks. Independent Motion Detection can only be accomplished if the egomotion detection yields a relevant background motion model. Therefore, the problem of Egomotion Detection is solved first by exploiting the topological structures of the global omnidirectional optical flow fields. The estimation of the optical flow field is the prerequisite of the Egomotion-Detection task. Since the omnidirectional projection introduces non-affine deformations on the image plane, the known optical flow calculation methods have to be modified to yield accurate results. This modification is introduced here as another subtask, Omnidirectional Optical Flow Estimation. The experiments concerning the 3D omnidirectional scene capturing are grouped under the fourth subtask 3D Omni-Image Processing.

The aim of this work is to design and implement a novel computer interface based on detection and tracking of a hand in an image from a single camera. The created interface doesn't require any special hardware and it is possible to use it on a common computer with standard web-camera. The implemented interface was used to create an application, which is able to synthesize keyboard and mouse input events and this way it is able to control existing programs without the need to change their source code. Another contribution of this work is a novel method of automatic data acquisition for training of hand detectors. By using this method it is possible to collect thousands of training examples in a few hours.

This thesis deals with the implementation of security guard software for OS Linux using an appropriate web camera. The main part of this application is process running in background using V4L application interface to communicate with web cam. Because this program uses dynamically loaded plug-ins for motion detection, it is very simple to change detection algorithm just by modifying configuration file. Application data can be saved as images or video files. Client application was created for online monitoring by user. It communicates with security guard software over network by TCP/IP protocol. Implemented application layer protocol allows simple client authentication and data encryption.

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Automatic detection of moving objects in video sequences is a widely researched topic with application in surveillance operations. Methods based on background cancellation by frame differencing are extremely common. However this process becomes much more complicated when the background is not completely stable due to camera motion. This thesis considers a space application where surveillance cameras around a shuttle launch site are used to detect any debris from the shuttle. The ground shake due to the impact of the launch causes the background to be shaky. We stabilize the background by translation of each frame, the optimum translation being determined by minimizing the energy difference between consecutive frames. This process is optimized by using a sub-image instead of the whole frame, the sub-image being chosen by taking an edge detection plot of the background and choosing the area with greatest density of edges as the sub-image of interest. The stabilized sequence is then processed by taking the difference between consecutive frames and marking areas with high intensity as the areas where motion is taking place. The residual noise from the background stabilization part is filtered out by masking the areas where the background has edges, as these areas have the highest probability of false alarms due to background motion.