We propose a method for guiding a photographer to rotate her his smartphone camera to obtain an image that overlaps with another image of the same scene. The other image is taken by another photographer from a different viewpoint. Our method is applicable even when the images do not have overlapping fields of view. Straightforward applications of our method include sharing attention to regions of interest for social purposes, or adding missing images to improve structure for motion results. Our solution uses additional images of the scene, which are often available since many people use their smartphone cameras regularly. These images may be available online from other photographers who are present at the scene. Our method avoids 3D scene reconstruction; it relies instead on a new representation that consists of the spatial orders of the scene points on two axes, x and y. This representation allows a sequence of points to be chosen efficiently and projected onto the photographers images, using epipolar point transfer. Overlaying these epipolar lines on the live preview of the camera produces a convenient interface to guide the user. The method was tested on challenging datasets of images and succeeded in guiding a photographer from one view to a non-overlapping destination view.
The camera guidance problem seemingly resides in the field of "active vision" @cite_16 , where the goal is to change the pose of controlled cameras, e.g., cameras mounted on robots or PTZ cameras, to allow the optimization of some objective. For example, when the objective is object tracking, fixation on objects over time is maintained through control of the camera pose. A general approach for the simultaneous tracking of multiple moving targets using a generic active stereo setup is studied by @cite_13 . As far as we know, none of the methods in the active vision field considered a set of casually taken photographs as in our scenario; moreover, the environment is usually strictly controlled in advance and then manipulated @cite_0 . Another drawback of these methods is the computational time, where full 3D reconstruction of the scene is usually given in advance, or exhaustively calculated. Our method may be also applied to robot collaboration.
@cite_0 @cite_16 @cite_13
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This paper presents a general approach for the simultaneous tracking of multiple moving targets using a generic active stereo setup. The problem is formulated on the plane, where cameras are modeled as “line scan cameras,” and targets are described as points with unconstrained motion. We propose to control the active system parameters in such a manner that the images of the targets in the two views are related by a homography. This homography is specified during the design stage and, thus, can be used to implicitly encode the desired tracking behavior. Such formulation leads to an elegant geometric framework that enables a systematic and thorough analysis of the problem at hand. The benefits of the approach are illustrated by applying the framework to two distinct stereo configurations. In the first case, we assume two pan-tilt-zoom cameras, with rotation and zoom control, which are arbitrarily placed in the working environment. It is proved that such a stereo setup can track up to N = 3 free-moving targets, while assuring that the image location of each target is the same for both views. The second example considers a robot head with neck pan motion and independent eye rotation. For this case, it is shown that it is not possible to track more than N = 2 targets because of the lack of zoom. The theoretical framework is used to derive the control equations, and the implementation of the tracking behavior is described in detail. The correctness of the results is confirmed through simulations and real tracking experiments. This paper presents a novel decision-theoretic approach to control and coordinate multiple active cameras for observing a number of moving targets in a surveillance system. This approach offers the advantages of being able to (a) account for the stochasticity of targets' motion via probabilistic modeling, and (b) address the trade-off between maximizing the expected number of observed targets and the resolution of the observed targets through stochastic optimization. One of the key issues faced by existing approaches in multi-camera surveillance is that of scalability with increasing number of targets. We show how its scalability can be improved by exploiting the problem structure: as proven analytically, our decision-theoretic approach incurs time that is linear in the number of targets to be observed during surveillance. As demonstrated empirically through simulations, our proposed approach can achieve high-quality surveillance of up to 50 targets in real time and its surveillance performance degrades gracefully with increasing number of targets. We also demonstrate our proposed approach with real AXIS 214 PTZ cameras in maximizing the number of Lego robots observed at high resolution over a surveyed rectangular area. The results are promising and clearly show the feasibility of our decision-theoretic approach in controlling and coordinating the active cameras in real surveillance system. In this paper, we control image collection for a mobile stereo camera that is actively localizing a group of mobile targets. In particular, assuming that at least one pair of stereo images of the targets is available, we propose a novel approach to control the rotation and translation of the stereo camera so that the next observation of the targets will minimize their localization uncertainty. We call this problem the Next-Best-View problem for mobile targets (mNBV). The advantage of using a stereo camera is that, using triangulation, the two simultaneous images taken by the robot during a single observation can yield range and bearing measurements of the targets, as well as their uncertainty. A Kalman filter fuses the full state history and covariance estimates, as more measurements are acquired. Our solution to the mNBV problem determines the relative transformations between camera and targets that will minimize the fused uncertainty of the targets' locations. We determine a motion plan that realizes the mNBV while respecting field of view constraints. In particular, with every new observation, we compute a new mNBV in the frame relative to the camera and subsequently realize this view in global coordinates via a gradient descent algorithm that also respects field of view constraints. Integration of mNBV with motion planning results in a hybrid system, which we illustrate in computer simulations. In this paper, we consider the problem of precisely localizing a group of stationary targets using a single stereo camera mounted on a mobile robot. In particular, assuming that at least one pair of stereo images of the targets is available, we seek to determine where to move the stereo camera so that the localization uncertainty of the targets is minimized. We call this problem the Next-Best-View problem. The advantage of using a stereo camera is that, using triangulation, the two simultaneous images can yield range and bearing measurements of the targets, as well as their uncertainty. We use a Kalman filter to fuse location and uncertainty estimates as more measurements are acquired. Our solution to the Next-Best-View problem is to iteratively minimize the fused uncertainty of the targets' locations subject to field-of-view constraints. We capture these objectives by appropriate artificial potentials on the camera's relative frame and the global frame, respectively. In particular, with every new observation, the mobile stereo camera computes the new next best view on the relative frame and subsequently realizes this view in the global frame via gradient descent on the space of robot positions and orientations, until a new observation is made. Integration of next best view with motion planning results in a hybrid system, which we illustrate in computer simulations. This paper considers the problem of sustained multicamera tracking in the presence of occlusion and changes in the target motion model. The key insight of the proposed method is the fact that, under mild conditions, the 2D trajectories of the target in the image planes of each of the cameras are constrained to evolve in the same subspace. This observation allows for identifying, at each time instant, a single (piecewise) linear model that explains all the available 2D measurements. In turn, this model can be used in the context of a modified particle filter to predict future target locations. In the case where the target is occluded to some of the cameras, the missing measurements can be estimated using the facts that they must lie both in the subspace spanned by previous measurements and satisfy epipolar constraints. Hence, by exploiting both dynamical and geometrical constraints the proposed method can robustly handle substantial occlusion, without the need for performing 3D reconstruction, calibrated cameras or constraints on sensor separation. The performance of the proposed tracker is illustrated with several challenging examples involving targets that substantially change appearance and motion models while occluded to some of the cameras. For robotic systems to realise lifelong autonomy they must be able to navigate accurately in changing environments. In this thesis we describe, implement and validate a new approach to the problem of long-term navigation. To begin, we present our stereo visual odometry system which provides highly accurate pose estimation. Our approach combines several techniques found in existing implementations and a recently published image descriptor that simplifies the solution architecture. The performance and versatility of our system is demonstrated through testing on multiple datasets. Equipped with our visual odometry system, we describe a new approach to the problem of lifelong navigation. We learn a model whose complexity varies naturally in accordance with the variation of scene appearance. As the robot repeatedly traverses its workspace, it accumulates distinct visual experiences that, in concert, implicitly represent the scene variation - each experience captures a visual mode. When operating in a previously visited area, we continually try to localise in these previous experiences while simultaneously running the visual odometry. Failure to localise in a sufficient number of prior experiences indicates an insufficient model of the workspace and instigates the laying down of the live image sequence as a new distinct experience. In this way, over time we can capture the typical temporally varying appearance of an environment and the number of experiences required tends to a constant. Although we focus on vision as a primary sensor, the ideas we present here are equally applicable to other sensor modalities. We demonstrate our approach working on a road vehicle operating over a three month period at different times of day, in different weather and lighting conditions. We present an approach to control a 6-degree-of-freedom (DOF) manipulator using an uncalibrated visual servoing (VS) approach that addresses the challenges of choosing proper image features for target objects and designing a VS controller to enhance the tracking performance. The main contribution of this paper is the definition of a new virtual visual space (image space). A novel stereo camera model employing virtual orthogonal cameras is used to map 6-D poses from Cartesian space to this virtual visual space . Each component of the 6-D pose vector defined in this virtual visual space is linearly independent, leading to a full-rank @math image Jacobian matrix , which allows avoiding classical problems, such as image space singularities and local minima. Furthermore, the control for rotational and translational motion of robot is decoupled due to the diagonal image Jacobian. Finally, simulation results with an eye-to-hand robotic system confirm the improvement in controller stability and motion performance with respect to conventional VS approaches. Experimental results on a 6-DOF industrial robot are provided to illustrate the effectiveness of the proposed method and the feasibility of using this method in practical scenarios. The authors present algorithms for robotic (eye-in-hand configuration) real-time visual tracking of arbitrary 3D objects traveling at unknown velocities in a 2D space (depth is given as known). Visual tracking is formulated as a problem of combining control with computer vision. A mathematical formulation of the control problem that includes information from a novel feedback vision sensor and represents everything with respect to the camera frame is presented. The sum-of-squared differences (SSD) optical flow is used to compute the vector of discrete displacements each instant of time. These displacements can be fed either directly to a PI (proportional-integral) controller or to a pole assignment controller or discrete steady-state Kalman filter. In the latter case, the Kalman filter calculates the estimated values of the system's states and the exogenous disturbances, and a discrete LQG (linear-quadratic Gaussian) controller computes the desired motion of the robotic system. The outputs of the controllers are sent to the Cartesian robotic controller. Performance results are presented. > Tracking across cameras with non-overlapping views is a challenging problem. Firstly, the observations of an object are often widely separated in time and space when viewed from non-overlapping cameras. Secondly, the appearance of an object in one camera view might be very different from its appearance in another camera view due to the differences in illumination, pose and camera properties. To deal with the first problem, we observe that people or vehicles tend to follow the same paths in most cases, i.e., roads, walkways, corridors etc. The proposed algorithm uses this conformity in the traversed paths to establish correspondence. The algorithm learns this conformity and hence the inter-camera relationships in the form of multivariate probability density of space-time variables (entry and exit locations, velocities, and transition times) using kernel density estimation. To handle the appearance change of an object as it moves from one camera to another, we show that all brightness transfer functions from a given camera to another camera lie in a low dimensional subspace. This subspace is learned by using probabilistic principal component analysis and used for appearance matching. The proposed approach does not require explicit inter-camera calibration, rather the system learns the camera topology and subspace of inter-camera brightness transfer functions during a training phase. Once the training is complete, correspondences are assigned using the maximum likelihood (ML) estimation framework using both location and appearance cues. Experiments with real world videos are reported which validate the proposed approach. We present a direct visual odometry algorithm for a fisheye-stereo camera. Our algorithm performs simultaneous camera motion estimation and semi-dense reconstruction. The pipeline consists of two threads: a tracking thread and a mapping thread. In the tracking thread, we estimate the camera pose via semi-dense direct image alignment. To have a wider field of view (FoV) which is important for robotic perception, we use fisheye images directly without converting them to conventional pinhole images which come with a limited FoV. To address the epipolar curve problem, plane-sweeping stereo is used for stereo matching and depth initialization. Multiple depth hypotheses are tracked for selected pixels to better capture the uncertainty characteristics of stereo matching. Temporal motion stereo is then used to refine the depth and remove false positive depth hypotheses. Our implementation runs at an average of 20 Hz on a low-end PC. We run experiments in outdoor environments to validate our algorithm, and discuss the experimental results. We experimentally show that we are able to estimate 6D poses with low drift, and at the same time, do semi-dense 3D reconstruction with high accuracy. To the best of our knowledge, there is no other existing semi-dense direct visual odometry algorithm for a fisheye-stereo camera. We propose a tracker for multiple interacting targets in a camera network.A model is developed to decide the group state of each trajectory.The tracking problem is converted to a network flow problem. In this paper we propose a framework for tracking multiple interacting targets in a wide-area camera network consisting of both overlapping and non-overlapping cameras. Our method is motivated from observations that both individuals and groups of targets interact with each other in natural scenes. We associate each raw target trajectory (i.e., a tracklet) with a group state, which indicates if the trajectory belongs to an individual or a group. Structural Support Vector Machine (SSVM) is applied to the group states to decide if merge or split events occur in the scene. Information fusion between multiple overlapping cameras is handled using a homography-based voting scheme. The problem of tracking multiple interacting targets is then converted to a network flow problem, for which the solution can be obtained by the K-shortest paths algorithm. We demonstrate the effectiveness of the proposed algorithm on the challenging VideoWeb dataset in which a large amount of multi-person interaction activities are present. Comparative analysis with state-of-the-art methods is also shown. We propose a novel approach for real-time object pose detection and tracking that is highly scalable in terms of the number of objects tracked and the number of cameras observing the scene. Key to this scalability is a high degree of parallelism in the algorithms employed. The method maintains a single 3D simulated model of the scene consisting of multiple objects together with a robot operating on them. This allows for rapid synthesis of appearance, depth, and occlusion information from each camera viewpoint. This information is used both for updating the pose estimates and for extracting the low-level visual cues. The visual cues obtained from each camera are efficiently fused back into the single consistent scene representation using a constrained optimization method. The centralized scene representation, together with the reliability measures it enables, simplify the interaction between pose tracking and pose detection across multiple cameras. We demonstrate the robustness of our approach in a realistic manipulation scenario. We publicly release this work as a part of a general ROS software framework for real-time pose estimation, SimTrack, that can be integrated easily for different robotic applications. How should a robot direct active vision so as to ensure reliable grasping? We answer this question for the case of dexterous grasping of unfamiliar objects. By dexterous grasping we simply mean grasping by any hand with more than two fingers, such that the robot has some choice about where to place each finger. Such grasps typically fail in one of two ways, either unmodeled objects in the scene cause collisions or object reconstruction is insufficient to ensure that the grasp points provide a stable force closure. These problems can be solved more easily if active sensing is guided by the anticipated actions. Our approach has three stages. First, we take a single view and generate candidate grasps from the resulting partial object reconstruction. Second, we drive the active vision approach to maximise surface reconstruction quality around the planned contact points. During this phase, the anticipated grasp is continually refined. Third, we direct gaze to improve the safety of the planned reach to grasp trajectory. We show, on a dexterous manipulator with a camera on the wrist, that our approach (80.4 success rate) outperforms a randomised algorithm (64.3 success rate). We present a distributed system for wide-area multi-object tracking across disjoint camera views. Every camera in the system performs multi-object tracking, and keeps its own trackers and trajectories. The data from multiple features are exchanged between adjacent cameras for object matching. We employ a probabilistic Petri Net-based approach to account for the uncertainties of the vision algorithms (such as unreliable background subtraction, and tracking failure) and to incorporate the available domain knowledge. We combine appearance features of objects as well as the travel-time evidence for target matching and consistent labeling across disjoint camera views. 3D color histogram, histogram of oriented gradients, local binary patterns, object size and aspect ratio are used as the appearance features. The distribution of the travel time is modeled by a Gaussian mixture model. Multiple features are combined by the weights, which are assigned based on the reliability of the features. By incorporating the domain knowledge about the camera configurations and the information about the received packets from other cameras, certain transitions are fired in the probabilistic Petri net. The system is trained to learn different parameters of the matching process, and updated online. We first present wide-area tracking of vehicles, where we used three non-overlapping cameras. The first and the third cameras are approximately 150 m apart from each other with two intersections in the blind region. We also present an example of applying our method to a people-tracking scenario. The results show the success of the proposed method. A comparison between our work and related work is also presented. Real-time, high-quality, 3D scanning of large-scale scenes is key to mixed reality and robotic applications. However, scalability brings challenges of drift in pose estimation, introducing significant errors in the accumulated model. Approaches often require hours of offline processing to globally correct model errors. Recent online methods demonstrate compelling results but suffer from (1) needing minutes to perform online correction, preventing true real-time use; (2) brittle frame-to-frame (or frame-to-model) pose estimation, resulting in many tracking failures; or (3) supporting only unstructured point-based representations, which limit scan quality and applicability. We systematically address these issues with a novel, real-time, end-to-end reconstruction framework. At its core is a robust pose estimation strategy, optimizing per frame for a global set of camera poses by considering the complete history of RGB-D input with an efficient hierarchical approach. We remove the heavy reliance on temporal tracking and continually localize to the globally optimized frames instead. We contribute a parallelizable optimization framework, which employs correspondences based on sparse features and dense geometric and photometric matching. Our approach estimates globally optimized (i.e., bundle adjusted) poses in real time, supports robust tracking with recovery from gross tracking failures (i.e., relocalization), and re-estimates the 3D model in real time to ensure global consistency, all within a single framework. Our approach outperforms state-of-the-art online systems with quality on par to offline methods, but with unprecedented speed and scan completeness. Our framework leads to a comprehensive online scanning solution for large indoor environments, enabling ease of use and high-quality results.1 High-speed smooth and accurate visual tracking of objects in arbitrary, unstructured environments is essential for robotics and human motion analysis. However, building a system that can adapt to arbitrary objects and a wide range of lighting conditions is a challenging problem, especially if hard real-time constraints apply like in robotics scenarios. In this work, we introduce a method for learning a discriminative object tracking system based on the recent structured regression framework for object localization. Using a kernel function that allows fast evaluation on the GPU, the resulting system can process video streams at speed of 100 frames per second or more. Consecutive frames in high speed video sequences are typically very redundant, and for training an object detection system, it is sufficient to have training labels from only a subset of all images. We propose an active learning method that select training examples in a data-driven way, thereby minimizing the required number of training labeling. Experiments on realistic data show that the active learning is superior to previously used methods for dataset subsampling for this task. We propose a novel solution to the generalized camera pose problem which includes the internal scale of the generalized camera as an unknown parameter. This further generalization of the well-known absolute camera pose problem has applications in multi-frame loop closure. While a well-calibrated camera rig has a fixed and known scale, camera trajectories produced by monocular motion estimation necessarily lack a scale estimate. Thus, when performing loop closure in monocular visual odometry, or registering separate structure-from-motion reconstructions, we must estimate a seven degree-of-freedom similarity transform from corresponding observations. Existing approaches solve this problem, in specialized configurations, by aligning 3D triangulated points or individual camera pose estimates. Our approach handles general configurations of rays and points and directly estimates the full similarity transformation from the 2D-3D correspondences. Four correspondences are needed in the minimal case, which has eight possible solutions. The minimal solver can be used in a hypothesize-and-test architecture for robust transformation estimation. Our solver also produces a least-squares estimate in the overdetermined case. The approach is evaluated experimentally on synthetic and real datasets, and is shown to produce higher accuracy solutions to multi-frame loop closure than existing approaches. Image based reconstruction of urban environments is a challenging problem that deals with optimization of large number of variables, and has several sources of errors like the presence of dynamic objects. Since most large scale approaches make the assumption of observing static scenes, dynamic objects are relegated to the noise modelling section of such systems. This is an approach of convenience since the RANSAC based framework used to compute most multiview geometric quantities for static scenes naturally confine dynamic objects to the class of outlier measurements. However, reconstructing dynamic objects along with the static environment helps us get a complete picture of an urban environment. Such understanding can then be used for important robotic tasks like path planning for autonomous navigation, obstacle tracking and avoidance, and other areas. In this paper, we propose a system for robust SLAM that works in both static and dynamic environments. To overcome the challenge of dynamic objects in the scene, we propose a new model to incorporate semantic constraints into the reconstruction algorithm. While some of these constraints are based on multi-layered dense CRFs trained over appearance as well as motion cues, other proposed constraints can be expressed as additional terms in the bundle adjustment optimization process that does iterative refinement of 3D structure and camera object motion trajectories. We show results on the challenging KITTI urban dataset for accuracy of motion segmentation and reconstruction of the trajectory and shape of moving objects relative to ground truth. We are able to show average relative error reduction by 41 for moving object trajectory reconstruction relative to state-of-the-art methods like TriTrack[16], as well as on standard bundle adjustment algorithms with motion segmentation. Monocular SLAM has the potential to turn inexpensive cameras into powerful pose sensors for applications such as robotics and augmented reality. We present a relocalization module for such systems which solves some of the problems encountered by previous monocular SLAM systems-tracking failure, map merging, and loop closure detection. This module extends recent advances in keypoint recognition to determine the camera pose relative to the landmarks within a single frame time of 33 ms. We first show how this module can be used to improve the robustness of these systems. Blur, sudden motion, and occlusion can all cause tracking to fail, leading to a corrupted map. Using the relocalization module, the system can automatically detect and recover from tracking failure while preserving map integrity. Extensive tests show that the system can then reliably generate maps for long sequences even in the presence of frequent tracking failure. We then show that the relocalization module can be used to recognize overlap in maps, i.e., when the camera has returned to a previously mapped area. Having established an overlap, we determine the relative pose of the maps using trajectory alignment so that independent maps can be merged and loop closure events can be recognized. The system combining all of these abilities is able to map larger environments and for significantly longer periods than previous systems. This paper is about long-term navigation in environments whose appearance changes over time, suddenly or gradually. We describe, implement and validate an approach which allows us to incrementally learn a model whose complexity varies naturally in accordance with variation of scene appearance. It allows us to leverage the state of the art in pose estimation to build over many runs, a world model of sufficient richness to allow simple localisation despite a large variation in conditions. As our robot repeatedly traverses its workspace, it accumulates distinct visual experiences that in concert, implicitly represent the scene variation: each experience captures a visual mode. When operating in a previously visited area, we continually try to localise in these previous experiences while simultaneously running an independent vision-based pose estimation system. Failure to localise in a sufficient number of prior experiences indicates an insufficient model of the workspace and instigates the laying down of the live image sequence as a new distinct experience. In this way, over time we can capture the typical time-varying appearance of an environment and the number of experiences required tends to a constant. Although we focus on vision as a primary sensor throughout, the ideas we present here are equally applicable to other sensor modalities. We demonstrate our approach working on a road vehicle operating over a 3-month period at different times of day, in different weather and lighting conditions. We present extensive results analysing different aspects of the system and approach, in total processing over 136,000 frames captured from 37 km of driving.