Illumination invariant stationary object detection

A real-time system for the detection and tracking of moving objects that becomes stationary in a restricted zone. A new pixel classification method based on the segmentation history image is used to identify stationary objects in the scene. These objects are then tracked using a novel adaptive edge orientation-based tracking method. Experimental results have shown that the tracking technique gives more than a 95% detection success rate, even if objects are partially occluded. The tracking results, together with the historic edge maps, are analysed to remove objects that are no longer stationary or are falsely identified as foreground regions because of sudden changes in the illumination conditions. The technique has been tested on over 7 h of video recorded at different locations and time of day, both outdoors and indoors. The results obtained are compared with other available state-of-the-art methods

VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING

These days, detection of Visual Attention Regions (VAR), such as moving objects has become an integral part of many Computer Vision applications, viz. pattern recognition, object detection and classification, video surveillance, autonomous driving, human-machine interaction (HMI), and so forth. The moving object identification using bounding boxes has matured to the level of localizing the objects along their rigid borders and the process is called foreground localization (FGL). Over the decades, many image segmentation methodologies have been well studied, devised, and extended to suit the video FGL. Despite that, still, the problem of video foreground (FG) segmentation remains an intriguing task yet appealing due to its ill-posed nature and myriad of applications. Maintaining spatial and temporal coherence, particularly at object boundaries, persists challenging, and computationally burdensome. It even gets harder when the background possesses dynamic nature, like swaying tree branches or shimmering water body, and illumination variations, shadows cast by the moving objects, or when the video sequences have jittery frames caused by vibrating or unstable camera mounts on a surveillance post or moving robot. At the same time, in the analysis of traffic flow or human activity, the performance of an intelligent system substantially depends on its robustness of localizing the VAR, i.e., the FG. To this end, the natural question arises as what is the best way to deal with these challenges? Thus, the goal of this thesis is to investigate plausible real-time performant implementations from traditional approaches to modern-day deep learning (DL) models for FGL that can be applicable to many video content-aware applications (VCAA). It focuses mainly on improving existing methodologies through harnessing multimodal spatial and temporal cues for a delineated FGL. The first part of the dissertation is dedicated for enhancing conventional sample-based and Gaussian mixture model (GMM)-based video FGL using probability mass function (PMF), temporal median filtering, and fusing CIEDE2000 color similarity, color distortion, and illumination measures, and picking an appropriate adaptive threshold to extract the FG pixels. The subjective and objective evaluations are done to show the improvements over a number of similar conventional methods. The second part of the thesis focuses on exploiting and improving deep convolutional neural networks (DCNN) for the problem as mentioned earlier. Consequently, three models akin to encoder-decoder (EnDec) network are implemented with various innovative strategies to improve the quality of the FG segmentation. The strategies are not limited to double encoding - slow decoding feature learning, multi-view receptive field feature fusion, and incorporating spatiotemporal cues through long-shortterm memory (LSTM) units both in the subsampling and upsampling subnetworks. Experimental studies are carried out thoroughly on all conditions from baselines to challenging video sequences to prove the effectiveness of the proposed DCNNs. The analysis demonstrates that the architectural efficiency over other methods while quantitative and qualitative experiments show the competitive performance of the proposed models compared to the state-of-the-art

Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network

Drone systems have been deployed by various law enforcement agencies to monitor hostiles, spy on foreign drug cartels, conduct border control operations, etc. This paper introduces a real-time drone surveillance system to identify violent individuals in public areas. The system first uses the Feature Pyramid Network to detect humans from aerial images. The image region with the human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network for human pose estimation. The orientations between the limbs of the estimated pose are next used to identify the violent individuals. The proposed deep network can learn meaningful representations quickly using ScatterNet and structural priors with relatively fewer labeled examples. The system detects the violent individuals in real-time by processing the drone images in the cloud. This research also introduces the aerial violent individual dataset used for training the deep network which hopefully may encourage researchers interested in using deep learning for aerial surveillance. The pose estimation and violent individuals identification performance is compared with the state-of-the-art techniques.Comment: To Appear in the Efficient Deep Learning for Computer Vision (ECV) workshop at IEEE Computer Vision and Pattern Recognition (CVPR) 2018. Youtube demo at this: https://www.youtube.com/watch?v=zYypJPJipY

Video analytics for security systems

This study has been conducted to develop robust event detection and object tracking algorithms that can be implemented in real time video surveillance applications. The aim of the research has been to produce an automated video surveillance system that is able to detect and report potential security risks with minimum human intervention. Since the algorithms are designed to be implemented in real-life scenarios, they must be able to cope with strong illumination changes and occlusions. The thesis is divided into two major sections. The first section deals with event detection and edge based tracking while the second section describes colour measurement methods developed to track objects in crowded environments. The event detection methods presented in the thesis mainly focus on detection and tracking of objects that become stationary in the scene. Objects such as baggage left in public places or vehicles parked illegally can cause a serious security threat. A new pixel based classification technique has been developed to detect objects of this type in cluttered scenes. Once detected, edge based object descriptors are obtained and stored as templates for tracking purposes. The consistency of these descriptors is examined using an adaptive edge orientation based technique. Objects are tracked and alarm events are generated if the objects are found to be stationary in the scene after a certain period of time. To evaluate the full capabilities of the pixel based classification and adaptive edge orientation based tracking methods, the model is tested using several hours of real-life video surveillance scenarios recorded at different locations and time of day from our own and publically available databases (i-LIDS, PETS, MIT, ViSOR). The performance results demonstrate that the combination of pixel based classification and adaptive edge orientation based tracking gave over 95% success rate. The results obtained also yield better detection and tracking results when compared with the other available state of the art methods. In the second part of the thesis, colour based techniques are used to track objects in crowded video sequences in circumstances of severe occlusion. A novel Adaptive Sample Count Particle Filter (ASCPF) technique is presented that improves the performance of the standard Sample Importance Resampling Particle Filter by up to 80% in terms of computational cost. An appropriate particle range is obtained for each object and the concept of adaptive samples is introduced to keep the computational cost down. The objective is to keep the number of particles to a minimum and only to increase them up to the maximum, as and when required. Variable standard deviation values for state vector elements have been exploited to cope with heavy occlusion. The technique has been tested on different video surveillance scenarios with variable object motion, strong occlusion and change in object scale. Experimental results show that the proposed method not only tracks the object with comparable accuracy to existing particle filter techniques but is up to five times faster. Tracking objects in a multi camera environment is discussed in the final part of the thesis. The ASCPF technique is deployed within a multi-camera environment to track objects across different camera views. Such environments can pose difficult challenges such as changes in object scale and colour features as the objects move from one camera view to another. Variable standard deviation values of the ASCPF have been utilized in order to cope with sudden colour and scale changes. As the object moves from one scene to another, the number of particles, together with the spread value, is increased to a maximum to reduce any effects of scale and colour change. Promising results are obtained when the ASCPF technique is tested on live feeds from four different camera views. It was found that not only did the ASCPF method result in the successful tracking of the moving object across different views but also maintained the real time frame rate due to its reduced computational cost thus indicating that the method is a potential practical solution for multi camera tracking applications

Video-based motion detection for stationary and moving cameras

In real world monitoring applications, moving object detection remains to be a challenging task due to factors such as background clutter and motion, illumination variations, weather conditions, noise, and occlusions. As a fundamental first step in many computer vision applications such as object tracking, behavior understanding, object or event recognition, and automated video surveillance, various motion detection algorithms have been developed ranging from simple approaches to more sophisticated ones. In this thesis, we present two moving object detection frameworks. The first framework is designed for robust detection of moving and static objects in videos acquired from stationary cameras. This method exploits the benefits of fusing a motion computation method based on spatio-temporal tensor formulation, a novel foreground and background modeling scheme, and a multi-cue appearance comparison. This hybrid system can handle challenges such as shadows, illumination changes, dynamic background, stopped and removed objects. Extensive testing performed on the CVPR 2014 Change Detection benchmark dataset shows that FTSG outperforms most state-of-the-art methods. The second framework adapts moving object detection to full motion videos acquired from moving airborne platforms. This framework has two main modules. The first module stabilizes the video with respect to a set of base-frames in the sequence. The stabilization is done by estimating four-point homographies using prominent feature (PF) block matching, motion filtering and RANSAC for robust matching. Once the frame to base frame homographies are available the flux tensor motion detection module using local second derivative information is applied to detect moving salient features. Spurious responses from the frame boundaries and other post- processing operations are applied to reduce the false alarms and produce accurate moving blob regions that will be useful for tracking

Segmentation mutuelle d'objets d'intérêt dans des séquences d'images stéréo multispectrales

Les systèmes de vidéosurveillance automatisés actuellement déployés dans le monde sont encore bien loin de ceux qui sont représentés depuis des années dans les oeuvres de sciencefiction. Une des raisons derrière ce retard de développement est le manque d’outils de bas niveau permettant de traiter les données brutes captées sur le terrain. Le pré-traitement de ces données sert à réduire la quantité d’information qui transige vers des serveurs centralisés, qui eux effectuent l’interprétation complète du contenu visuel capté. L’identification d’objets d’intérêt dans les images brutes à partir de leur mouvement est un exemple de pré-traitement qui peut être réalisé. Toutefois, dans un contexte de vidéosurveillance, une méthode de pré-traitement ne peut généralement pas se fier à un modèle d’apparence ou de forme qui caractérise ces objets, car leur nature exacte n’est pas connue d’avance. Cela complique donc l’élaboration des méthodes de traitement de bas niveau. Dans cette thèse, nous présentons différentes méthodes permettant de détecter et de segmenter des objets d’intérêt à partir de séquences vidéo de manière complètement automatisée. Nous explorons d’abord les approches de segmentation vidéo monoculaire par soustraction d’arrière-plan. Ces approches se basent sur l’idée que l’arrière-plan d’une scène peut être modélisé au fil du temps, et que toute variation importante d’apparence non prédite par le modèle dévoile en fait la présence d’un objet en intrusion. Le principal défi devant être relevé par ce type de méthode est que leur modèle d’arrière-plan doit pouvoir s’adapter aux changements dynamiques des conditions d’observation de la scène. La méthode conçue doit aussi pouvoir rester sensible à l’apparition de nouveaux objets d’intérêt, malgré cette robustesse accrue aux comportements dynamiques prévisibles. Nous proposons deux méthodes introduisant différentes techniques de modélisation qui permettent de mieux caractériser l’apparence de l’arrière-plan sans que le modèle soit affecté par les changements d’illumination, et qui analysent la persistance locale de l’arrière-plan afin de mieux détecter les objets d’intérêt temporairement immobilisés. Nous introduisons aussi de nouveaux mécanismes de rétroaction servant à ajuster les hyperparamètres de nos méthodes en fonction du dynamisme observé de la scène et de la qualité des résultats produits.----------ABSTRACT: The automated video surveillance systems currently deployed around the world are still quite far in terms of capabilities from the ones that have inspired countless science fiction works over the past few years. One of the reasons behind this lag in development is the lack of lowlevel tools that allow raw image data to be processed directly in the field. This preprocessing is used to reduce the amount of information transferred to centralized servers that have to interpret the captured visual content for further use. The identification of objects of interest in raw images based on motion is an example of a reprocessing step that might be required by a large system. However, in a surveillance context, the preprocessing method can seldom rely on an appearance or shape model to recognize these objects since their exact nature cannot be known exactly in advance. This complicates the elaboration of low-level image processing methods. In this thesis, we present different methods that detect and segment objects of interest from video sequences in a fully unsupervised fashion. We first explore monocular video segmentation approaches based on background subtraction. These approaches are based on the idea that the background of an observed scene can be modeled over time, and that any drastic variation in appearance that is not predicted by the model actually reveals the presence of an intruding object. The main challenge that must be met by background subtraction methods is that their model should be able to adapt to dynamic changes in scene conditions. The designed methods must also remain sensitive to the emergence of new objects of interest despite this increased robustness to predictable dynamic scene behaviors. We propose two methods that introduce different modeling techniques to improve background appearance description in an illumination-invariant way, and that analyze local background persistence to improve the detection of temporarily stationary objects. We also introduce new feedback mechanisms used to adjust the hyperparameters of our methods based on the observed dynamics of the scene and the quality of the generated output

Airborne laser scanning raster data visualization

This guide provides an insight into a range of visualization techniques for high-resolution digital elevation models (DEMs). It is provided in the context of investigation and interpretation of various types of historical and modern, cultural and natural small-scale relief features and landscape structures. It also provides concise guidance for selecting the best techniques when looking at a specific type of landscape and/or looking for particular kinds of forms.The three main sections – descriptions of visualization techniques, guidance for selection of the techniques, and visualization tools – accompany examples of visualizations, exemplar archaeological and geomorphological case studies, a glossary of terms, and a list of references and recommendations for further reading. The structure facilitates people of different academic background and level of expertise to understand different visualizations, how to read them, how to manipulate the settings in a calculation, and choose the best suited for the purpose of the intended investigation.A smaller amount of books is also available in hardcover (ISBN 978-961-05-0011-7, 24 EUR).Monografija nudi vpogled v nabor tehnik prikaza visokoločljivih modelov višin. Napisana je v kontekstu preučevanja in interpretacije različnih tipov zgodovinskih in modernih, kulturnih in naravnih majhnih reliefnih oblik. Daje jedrnate napotke za izbiro najboljših tehnik prikaza določenih tipov pokrajine in izrazitih oblik.Tri glavna poglavja – opis tehnik prikazovanja digitalnih modelov višin, napotki za njihovo izbiro in orodja za izračun prikazov –, spremljajo izbrani primeri tipičnih arheoloških in geomorfoloških študij, slovarček pojmov ter seznam literature in priporočenega branja. Posameznikom z različnih znanstvenih področij in z različnim predznanjem o tematiki je struktura v pomoč pri razumevanju različnih tehnik prikazov, kako jih brati, kako izbrati prave nastavitve pri njihovem izračunu in kako prepoznati najbolj primerne za namen zasnovane raziskave