A novel camera autocalibration method is presented. Any camera model can be calibrated, and no calibration targets like checkerboards are used. The method requires the camera to be mounted on a lidar-equipped moving platform travelling through a structured environment along a known path. The primary reason for cross-modal camera calibration is not to solve the sensor fusion problem, but to tap the huge amount of accurate metric data points available from the lidar. The amount of measurements is easily four orders of magnitude higher than in checkerboard based methods. This leads to improved estimation accuracy, especially of higher-order distortion coefficients. In a multi-camera setup, the lidar additionally defines a common reference coordinate system for all cameras.Compared to the majority of published methods on camera-lidar autocalibration, (i) our calibration procedure relies on motion features, (ii) the hard-to-obtain-accurately lidar-lidar and lidar-image feature correspondences are not required, and (iii) both camera extrinsics and intrinsics, including complex distortion models, are autocalibrated. Experiments show that the calibration accuracy reaches or exceeds the accuracy of methods relying on calibration targets.

We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e. translation and rotation, of texture-less rigid objects. The dataset features thirty industry-relevant objects with no significant texture and no discriminative color or reflectance properties. The objects exhibit symmetries and mutual similarities in shape and/or size. Compared to other datasets, a unique property is that some of the objects are parts of others. The dataset includes training and test images that were captured with three synchronized sensors, specifically a structured-light and a time-of-flight RGB-D sensor and a high-resolution RGB camera. There are approximately 39K training and 10K test images from each sensor. Additionally, two types of 3D models are provided for each object, i.e. a manually created CAD model and a semi-automatically reconstructed one. Training images depict individual objects against a black background. Test images originate from twenty test scenes having varying complexity, which increases from simple scenes with several isolated objects to very challenging ones with multiple instances of several objects and with a high amount of clutter and occlusion. The images were captured from a systematically sampled view sphere around the object/scene, and are annotated with accurate ground truth 6D poses of all modeled objects. Initial evaluation results indicate that the state of the art in 6D object pose estimation has ample room for improvement, especially in difficult cases with significant occlusion. The T-LESS dataset is available online at cmp:felk:cvut:cz/t-less.

Although the positive effects of exercise on the well-being and quality of independent living for older adults are well accepted, many elderly individuals lack access to exercise facilities, or the skills and motivation to perform exercise at home. To provide a more engaging environment that promotes physical activity, various fitness applications have been proposed. Many of the available products, however, are geared toward a younger population and are not appropriate or engaging for an older population. To address these issues, we developed an automated interactive exercise coaching system using the Microsoft Kinect. The coaching system guides users through a series of video exercises, tracks and measures their movements, provides real-time feedback, and records their performance over time. Our system consists of exercises to improve balance, flexibility, strength, and endurance, with the aim of reducing fall risk and improving performance of daily activities. In this paper, we report on the development of the exercise system, discuss the results of our recent field pilot study with six independently living elderly individuals, and highlight the lessons learned relating to the in-home system setup, user tracking, feedback, and exercise performance evaluation.

Despite their ubiquitous presence, texture-less objects present significant challenges to contemporary visual object detection and localization algorithms. This paper proposes a practical method for the detection and accurate 3D localization of multiple texture-less and rigid objects depicted in RGB-D images. The detection procedure adopts the sliding window paradigm, with an efficient cascade-style evaluation of each window location. A simple pre-filtering is performed first, rapidly rejecting most locations. For each remaining location, a set of candidate templates (i.e. trained object views) is identified with a voting procedure based on hashing, which makes the method's computational complexity largely unaffected by the total number of known objects. The candidate templates are then verified by matching feature points in different modalities. Finally, the approximate object pose associated with each detected template is used as a starting point for a stochastic optimization procedure that estimates accurate 3D pose. Experimental evaluation shows that the proposed method yields a recognition rate comparable to the state of the art, while its complexity is sub-linear in the number of templates.

We present a procedure for egomotion estimation from visual input of a stereo pair of video cameras. The 3D egomotion problem, which has six degrees of freedom in general, is simplified to four dimensions and further decomposed to two two-dimensional subproblems. The decomposition allows us to use a voting strategy to identify the most probable solution, avoiding the random sampling (RANSAC) or other approximation techniques. The input constitutes of image correspondences between consecutive stereo pairs, i.e. feature points do not need to be tracked over time. The experiments show that even if a trajectory is put together as a simple concatenation of frame-to-frame increments, it comes out reliable and precise.

We propose a new affine-covariant feature, the stable affine frame (SAF). SAFs lie on the boundary of extremal regions, i.e. on isophotes. Instead of requiring the whole isophote to be stable with respect to intensity perturbation as in maximally stable extremal regions (MSERs), stability is required only locally, for the primitives constituting the three-point frames. The primitives are extracted by an affine invariant process that exploits properties of bitangents and algebraic moments. Thus, instead of using closed stable isophotes, i.e. MSERs, and detecting affine frames on them, SAFs are sought even on some unstable extremal regions. We show experimentally on standard datasets that SAFs have repeatability comparable to the best affine covariant detectors tested in the state-of-the-art report (Mikolajczyk et al., 2005) and consistently produce a significantly higher number of features per image. Moreover, the features cover images more evenly than MSERs, which facilitates robustness to occlusion.

Realistic approaches to large scale object recognition, i.e. for detection and localisation of hundreds or more objects, must support sub-linear time indexing.In the paper, we propose a method capable of recognising one of N objects in log(N) time.The "visual memory" is organised as a binary decision tree that is built to minimise average time to decision.Leaves of the tree represent a few local image areas, and each non-terminal node is associated with a 'weak classifier'.In the recognition phase, a single invariant measurement decides in which subtree a corresponding image area is sought.The method preserves all the strengths of local affine region methodsrobustness to background clutter, occlusion, and large changes of viewpoints.Experimentally we show that it supports near real-time recognition of hundreds of objects with state-of-the-art recognition rates.After the test image is processed (in a second on a current PCs), the recognition via indexing into the visual memory requires milliseconds.

A novel approach to appearance based object recognition is introduced. The proposed method, based on matching of local image features, reliably recognises objects under very different viewing conditions. First, distinguished regions of data-dependent shape are robustly detected. On these regions, local affine frames are established using several affine invariant constructions. Direct comparison of photometrically normalised colour intensities in local, geometrically aligned frames results in a matching scheme that is invariant to piecewise-affine image deformations, but still remains very discriminative. The potential of the approach is experimentally verified on COIL-100 and SOIL-47 – publicly available image databases. On SOIL-47, 100 % recognition rate is achieved for single training view per object. On COIL-100, 99.9% recognition rate is obtained for 18 training views per object. Robustness to severe occlusions is demonstrated by only a moderate decrease of recognition performance in an experiment where half of each test image is erased. 1