Multi-camera cooperative scene interpretation

In our society, video processing has become a convenient and widely used tool to assist, protect and simplify the daily life of people in areas such as surveillance and video conferencing. The growing number of cameras, the handling and analysis of these vast amounts of video data enable the development of multi-camera applications that cooperatively use multiple sensors. In many applications, bandwidth constraints, privacy issues, and difficulties in storing and analyzing large amounts of video data make applications costly and technically challenging. In this thesis, we deploy techniques ranging from low-level to high-level approaches, specifically designed for multi-camera networks. As a low-level approach, we designed a novel low-level foreground detection algorithm for real-time tracking applications, concentrating on difficult and changing illumination conditions. The main part of this dissertation focuses on a detailed analysis of two novel state-of-the-art real-time tracking approaches: a multi-camera tracking approach based on occupancy maps and a distributed multi-camera tracking approach with a feedback loop. As a high-level application we propose an approach to understand the dynamics in meetings - so called, smart meetings - using a multi-camera setup, consisting of fixed ambient and portable close-up cameras. For all method, we provided qualitative and quantitative results on several experiments, compared to state-of-the-art methods

WSD: Wild Selfie Dataset for Face Recognition in Selfie Images

With the rise of handy smart phones in the recent years, the trend of capturing selfie images is observed. Hence efficient approaches are required to be developed for recognising faces in selfie images. Due to the short distance between the camera and face in selfie images, and the different visual effects offered by the selfie apps, face recognition becomes more challenging with existing approaches. A dataset is needed to be developed to encourage the study to recognize faces in selfie images. In order to alleviate this problem and to facilitate the research on selfie face images, we develop a challenging Wild Selfie Dataset (WSD) where the images are captured from the selfie cameras of different smart phones, unlike existing datasets where most of the images are captured in controlled environment. The WSD dataset contains 45,424 images from 42 individuals (i.e., 24 female and 18 male subjects), which are divided into 40,862 training and 4,562 test images. The average number of images per subject is 1,082 with minimum and maximum number of images for any subject are 518 and 2,634, respectively. The proposed dataset consists of several challenges, including but not limited to augmented reality filtering, mirrored images, occlusion, illumination, scale, expressions, view-point, aspect ratio, blur, partial faces, rotation, and alignment. We compare the proposed dataset with existing benchmark datasets in terms of different characteristics. The complexity of WSD dataset is also observed experimentally, where the performance of the existing state-of-the-art face recognition methods is poor on WSD dataset, compared to the existing datasets. Hence, the proposed WSD dataset opens up new challenges in the area of face recognition and can be beneficial to the community to study the specific challenges related to selfie images and develop improved methods for face recognition in selfie images

VISUAL TRACKING AND ILLUMINATION RECOVERY VIA SPARSE REPRESENTATION

Compressive sensing, or sparse representation, has played a fundamental role in many fields of science. It shows that the signals and images can be reconstructed from far fewer measurements than what is usually considered to be necessary. Sparsity leads to efficient estimation, efficient compression, dimensionality reduction, and efficient modeling. Recently, there has been a growing interest in compressive sensing in computer vision and it has been successfully applied to face recognition, background subtraction, object tracking and other problems. Sparsity can be achieved by solving the compressive sensing problem using L1 minimization. In this dissertation, we present the results of a study of applying sparse representation to illumination recovery, object tracking, and simultaneous tracking and recognition. Illumination recovery, also known as inverse lighting, is the problem of recovering an illumination distribution in a scene from the appearance of objects located in the scene. It is used for Augmented Reality, where the virtual objects match the existing image and cast convincing shadows on the real scene rendered with the recovered illumination. Shadows in a scene are caused by the occlusion of incoming light, and thus contain information about the lighting of the scene. Although shadows have been used in determining the 3D shape of the object that casts shadows onto the scene, few studies have focused on the illumination information provided by the shadows. In this dissertation, we recover the illumination of a scene from a single image with cast shadows given the geometry of the scene. The images with cast shadows can be quite complex and therefore cannot be well approximated by low-dimensional linear subspaces. However, in this study we show that the set of images produced by a Lambertian scene with cast shadows can be efficiently represented by a sparse set of images generated by directional light sources. We first model an image with cast shadows as composed of a diffusive part (without cast shadows) and a residual part that captures cast shadows. Then, we express the problem in an L1-regularized least squares formulation, with nonnegativity constraints (as light has to be nonnegative at any point in space). This sparse representation enjoys an effective and fast solution, thanks to recent advances in compressive sensing. In experiments on both synthetic and real data, our approach performs favorably in comparison to several previously proposed methods. Visual tracking, which consistently infers the motion of a desired target in a video sequence, has been an active and fruitful research topic in computer vision for decades. It has many practical applications such as surveillance, human computer interaction, medical imaging and so on. Many challenges to design a robust tracking algorithm come from the enormous unpredictable variations in the target, such as deformations, fast motion, occlusions, background clutter, and lighting changes. To tackle the challenges posed by tracking, we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, noise and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an L1-regularized least squares problem. Then the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. Three additional components further improve the robustness of our approach: 1) a velocity incorporated motion model that helps concentrate the samples on the true target location in the next frame, 2) the nonnegativity constraints that help filter out clutter that is similar to tracked targets in reversed intensity patterns, and 3) a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure. We test the proposed approach on many challenging sequences involving heavy occlusions, drastic illumination changes, large scale changes, non-rigid object movement, out-of-plane rotation, and large pose variations. The proposed approach shows excellent performance in comparison with four previously proposed trackers. We also extend the work to simultaneous tracking and recognition in vehicle classification in IR video sequences. We attempt to resolve the uncertainties in tracking and recognition at the same time by introducing a static template set that stores target images in various conditions such as different poses, lighting, and so on. The recognition results at each frame are propagated to produce the final result for the whole video. The tracking result is evaluated at each frame and low confidence in tracking performance initiates a new cycle of tracking and classification. We demonstrate the robustness of the proposed method on vehicle tracking and classification using outdoor IR video sequences

Particle Filters for Colour-Based Face Tracking Under Varying Illumination

Automatic human face tracking is the basis of robotic and active vision systems used for facial feature analysis, automatic surveillance, video conferencing, intelligent transportation, human-computer interaction and many other applications. Superior human face tracking will allow future safety surveillance systems which monitor drowsy drivers, or patients and elderly people at the risk of seizure or sudden falls and will perform with lower risk of failure in unexpected situations. This area has actively been researched in the current literature in an attempt to make automatic face trackers more stable in challenging real-world environments. To detect faces in video sequences, features like colour, texture, intensity, shape or motion is used. Among these feature colour has been the most popular, because of its insensitivity to orientation and size changes and fast process-ability. The challenge of colour-based face trackers, however, has been dealing with the instability of trackers in case of colour changes due to the drastic variation in environmental illumination. Probabilistic tracking and the employment of particle filters as powerful Bayesian stochastic estimators, on the other hand, is increasing in the visual tracking field thanks to their ability to handle multi-modal distributions in cluttered scenes. Traditional particle filters utilize transition prior as importance sampling function, but this can result in poor posterior sampling. The objective of this research is to investigate and propose stable face tracker capable of dealing with challenges like rapid and random motion of head, scale changes when people are moving closer or further from the camera, motion of multiple people with close skin tones in the vicinity of the model person, presence of clutter and occlusion of face. The main focus has been on investigating an efficient method to address the sensitivity of the colour-based trackers in case of gradual or drastic illumination variations. The particle filter is used to overcome the instability of face trackers due to nonlinear and random head motions. To increase the traditional particle filter\u27s sampling efficiency an improved version of the particle filter is introduced that considers the latest measurements. This improved particle filter employs a new colour-based bottom-up approach that leads particles to generate an effective proposal distribution. The colour-based bottom-up approach is a classification technique for fast skin colour segmentation. This method is independent to distribution shape and does not require excessive memory storage or exhaustive prior training. Finally, to address the adaptability of the colour-based face tracker to illumination changes, an original likelihood model is proposed based of spatial rank information that considers both the illumination invariant colour ordering of a face\u27s pixels in an image or video frame and the spatial interaction between them. The original contribution of this work lies in the unique mixture of existing and proposed components to improve colour-base recognition and tracking of faces in complex scenes, especially where drastic illumination changes occur. Experimental results of the final version of the proposed face tracker, which combines the methods developed, are provided in the last chapter of this manuscript

Robust and real-time hand detection and tracking in monocular video

In recent years, personal computing devices such as laptops, tablets and smartphones have become ubiquitous. Moreover, intelligent sensors are being integrated into many consumer devices such as eyeglasses, wristwatches and smart televisions. With the advent of touchscreen technology, a new human-computer interaction (HCI) paradigm arose that allows users to interface with their device in an intuitive manner. Using simple gestures, such as swipe or pinch movements, a touchscreen can be used to directly interact with a virtual environment. Nevertheless, touchscreens still form a physical barrier between the virtual interface and the real world. An increasingly popular field of research that tries to overcome this limitation, is video based gesture recognition, hand detection and hand tracking. Gesture based interaction allows the user to directly interact with the computer in a natural manner by exploring a virtual reality using nothing but his own body language. In this dissertation, we investigate how robust hand detection and tracking can be accomplished under real-time constraints. In the context of human-computer interaction, real-time is defined as both low latency and low complexity, such that a complete video frame can be processed before the next one becomes available. Furthermore, for practical applications, the algorithms should be robust to illumination changes, camera motion, and cluttered backgrounds in the scene. Finally, the system should be able to initialize automatically, and to detect and recover from tracking failure. We study a wide variety of existing algorithms, and propose significant improvements and novel methods to build a complete detection and tracking system that meets these requirements. Hand detection, hand tracking and hand segmentation are related yet technically different challenges. Whereas detection deals with finding an object in a static image, tracking considers temporal information and is used to track the position of an object over time, throughout a video sequence. Hand segmentation is the task of estimating the hand contour, thereby separating the object from its background. Detection of hands in individual video frames allows us to automatically initialize our tracking algorithm, and to detect and recover from tracking failure. Human hands are highly articulated objects, consisting of finger parts that are connected with joints. As a result, the appearance of a hand can vary greatly, depending on the assumed hand pose. Traditional detection algorithms often assume that the appearance of the object of interest can be described using a rigid model and therefore can not be used to robustly detect human hands. Therefore, we developed an algorithm that detects hands by exploiting their articulated nature. Instead of resorting to a template based approach, we probabilistically model the spatial relations between different hand parts, and the centroid of the hand. Detecting hand parts, such as fingertips, is much easier than detecting a complete hand. Based on our model of the spatial configuration of hand parts, the detected parts can be used to obtain an estimate of the complete hand's position. To comply with the real-time constraints, we developed techniques to speed-up the process by efficiently discarding unimportant information in the image. Experimental results show that our method is competitive with the state-of-the-art in object detection while providing a reduction in computational complexity with a factor 1 000. Furthermore, we showed that our algorithm can also be used to detect other articulated objects such as persons or animals and is therefore not restricted to the task of hand detection. Once a hand has been detected, a tracking algorithm can be used to continuously track its position in time. We developed a probabilistic tracking method that can cope with uncertainty caused by image noise, incorrect detections, changing illumination, and camera motion. Furthermore, our tracking system automatically determines the number of hands in the scene, and can cope with hands entering or leaving the video canvas. We introduced several novel techniques that greatly increase tracking robustness, and that can also be applied in other domains than hand tracking. To achieve real-time processing, we investigated several techniques to reduce the search space of the problem, and deliberately employ methods that are easily parallelized on modern hardware. Experimental results indicate that our methods outperform the state-of-the-art in hand tracking, while providing a much lower computational complexity. One of the methods used by our probabilistic tracking algorithm, is optical flow estimation. Optical flow is defined as a 2D vector field describing the apparent velocities of objects in a 3D scene, projected onto the image plane. Optical flow is known to be used by many insects and birds to visually track objects and to estimate their ego-motion. However, most optical flow estimation methods described in literature are either too slow to be used in real-time applications, or are not robust to illumination changes and fast motion. We therefore developed an optical flow algorithm that can cope with large displacements, and that is illumination independent. Furthermore, we introduce a regularization technique that ensures a smooth flow-field. This regularization scheme effectively reduces the number of noisy and incorrect flow-vector estimates, while maintaining the ability to handle motion discontinuities caused by object boundaries in the scene. The above methods are combined into a hand tracking framework which can be used for interactive applications in unconstrained environments. To demonstrate the possibilities of gesture based human-computer interaction, we developed a new type of computer display. This display is completely transparent, allowing multiple users to perform collaborative tasks while maintaining eye contact. Furthermore, our display produces an image that seems to float in thin air, such that users can touch the virtual image with their hands. This floating imaging display has been showcased on several national and international events and tradeshows. The research that is described in this dissertation has been evaluated thoroughly by comparing detection and tracking results with those obtained by state-of-the-art algorithms. These comparisons show that the proposed methods outperform most algorithms in terms of accuracy, while achieving a much lower computational complexity, resulting in a real-time implementation. Results are discussed in depth at the end of each chapter. This research further resulted in an international journal publication; a second journal paper that has been submitted and is under review at the time of writing this dissertation; nine international conference publications; a national conference publication; a commercial license agreement concerning the research results; two hardware prototypes of a new type of computer display; and a software demonstrator

Video Object Recognition and Modeling by SIFT Matching Optimization

In this paper we present a novel technique for object modeling and object recognition in video. Given a set of videos containing 360 degrees views of objects we compute a model for each object, then we analyze short videos to determine if the object depicted in the video is one of the modeled objects. The object model is built from a video spanning a 360 degree view of the object taken against a uniform background. In order to create the object model, the proposed techniques selects a few representative frames from each video and local features of such frames. The object recognition is performed selecting a few frames from the query video, extracting local features from each frame and looking for matches in all the representative frames constituting the models of all the objects. If the number of matches exceed a fixed threshold the corresponding object is considered the recognized objects .To evaluate our approach we acquired a dataset of 25 videos representing 25 different objects and used these videos to build the objects model. Then we took 25 test videos containing only one of the known objects and 5 videos containing only unknown objects. Experiments showed that, despite a significant compression in the model, recognition results are satisfactory