Robust moving object detection by information fusion from multiple cameras

Moving object detection is an essential process before tracking and event recognition in video surveillance can take place. To monitor a wider field of view and avoid occlusions in pedestrian tracking, multiple cameras are usually used and homography can be employed to associate multiple camera views. Foreground regions detected from each of the multiple camera views are projected into a virtual top view according to the homography for a plane. The intersection regions of the foreground projections indicate the locations of moving objects on that plane. The homography mapping for a set of parallel planes at different heights can increase the robustness of the detection. However, homography mapping is very time consuming and the intersections of non-corresponding foreground regions can cause false-positive detections. In this thesis, a real-time moving object detection algorithm using multiple cameras is proposed. Unlike the pixelwise homography mapping which projects binary foreground images, the approach used in the research described in this thesis was to approximate the contour of each foreground region with a polygon and only transmit and project the polygon vertices. The foreground projections are rebuilt from the projected polygons in the reference view. The experimental results have shown that this method can be run in real time and generate results similar to those using foreground images. To identify the false-positive detections, both geometrical information and colour cues are utilized. The former is a height matching algorithm based on the geometry between the camera views. The latter is a colour matching algorithm based on the Mahalanobis distance of the colour distributions of two foreground regions. Since the height matching is uncertain in the scenarios with the adjacent pedestrian and colour matching cannot handle occluded pedestrians, the two algorithms are combined to improve the robustness of the foreground intersection classification. The robustness of the proposed algorithm is demonstrated in real-world image sequences

Global Optimisation of Multi‐Camera Moving Object Detection

An important task in intelligent video surveillance is to detect multiple pedestrians. These pedestrians may be occluded by each other in a camera view. To overcome this problem, multiple cameras can be deployed to provide complementary information, and homography mapping has been widely used for the association and fusion of multi‐camera observations. The intersection regions of the foreground projections usually indicate the locations of moving objects. However, many false positives may be generated from the intersections of non‐corresponding foreground regions. In this thesis, an algorithm for multi‐camera pedestrian detection is proposed. The first stage of this work is to propose pedestrian candidate locations on the top view. Two approaches are proposed in this stage. The first approach is a top‐down approach which is based on the probabilistic occupancy map framework. The ground plane is discretized into a grid, and the likelihood of pedestrian presence at each location is estimated by comparing a rectangle, of the average size of the pedestrians standing there, with the foreground silhouettes in all camera views. The second approach is a bottom‐up approach, which is based on the multi‐plane homography mapping. The foreground regions in all camera views are projected and overlaid in the top view according to the multi‐plane homographies and the potential locations of pedestrians are estimated from the intersection regions. In the second stage, where we borrowed the idea from the Quine‐McCluskey (QM) method for logic function minimisation, essential candidates are initially identified, each of which covers at least a significant part of the foreground that is not covered by the other candidates. Then non‐essential candidates are selected to cover the remaining foregrounds by following a repeated process, which alternates between merging redundant candidates and finding emerging essential candidates. Then, an alternative approach to the QM method, the Petrick’s method, is used for finding the minimum set of pedestrian candidates to cover all the foreground regions. These two methods are non‐iterative and can greatly increase the computational speed. No similar work has been proposed before. Experiments on benchmark video datasets have demonstrated the good performance of the proposed algorithm in comparison with other state‐of‐the‐art methods for pedestrian detection

Multigranularity Representations for Human Inter-Actions: Pose, Motion and Intention

Tracking people and their body pose in videos is a central problem in computer vision. Standard tracking representations reason about temporal coherence of detected people and body parts. They have difficulty tracking targets under partial occlusions or rare body poses, where detectors often fail, since the number of training examples is often too small to deal with the exponential variability of such configurations. We propose tracking representations that track and segment people and their body pose in videos by exploiting information at multiple detection and segmentation granularities when available, whole body, parts or point trajectories. Detections and motion estimates provide contradictory information in case of false alarm detections or leaking motion affinities. We consolidate contradictory information via graph steering, an algorithm for simultaneous detection and co-clustering in a two-granularity graph of motion trajectories and detections, that corrects motion leakage between correctly detected objects, while being robust to false alarms or spatially inaccurate detections. We first present a motion segmentation framework that exploits long range motion of point trajectories and large spatial support of image regions. We show resulting video segments adapt to targets under partial occlusions and deformations. Second, we augment motion-based representations with object detection for dealing with motion leakage. We demonstrate how to combine dense optical flow trajectory affinities with repulsions from confident detections to reach a global consensus of detection and tracking in crowded scenes. Third, we study human motion and pose estimation. We segment hard to detect, fast moving body limbs from their surrounding clutter and match them against pose exemplars to detect body pose under fast motion. We employ on-the-fly human body kinematics to improve tracking of body joints under wide deformations. We use motion segmentability of body parts for re-ranking a set of body joint candidate trajectories and jointly infer multi-frame body pose and video segmentation. We show empirically that such multi-granularity tracking representation is worthwhile, obtaining significantly more accurate multi-object tracking and detailed body pose estimation in popular datasets

Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

The joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, and the Vision and Fusion Laboratory (Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)), is organized annually since 2005 with the aim to report on the latest research and development findings of the doctoral students of both institutions. This book provides a collection of 16 technical reports on the research results presented on the 2009 workshop

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis