882 research outputs found
Relating vanishing points to catadioptric camera calibration
This paper presents the analysis and derivation of the geometric relation between vanishing points and camera parameters of central catadioptric camera systems. These vanishing points correspond to the three mutually orthogonal directions of 3D real world coordinate system (i.e. X, Y and Z axes). Compared to vanishing points (VPs) in the perspective projection, the advantages of VPs under central catadioptric projection are that there are normally two vanishing points for each set of parallel lines, since lines are projected to conics in the catadioptric image plane. Also, their vanishing points are usually located inside the image frame. We show that knowledge of the VPs corresponding to XYZ axes from a single image can lead to simple derivation of both intrinsic and extrinsic parameters of the central catadioptric system. This derived novel theory is demonstrated and tested on both synthetic and real data with respect to noise sensitivity
Under vehicle perception for high level safety measures using a catadioptric camera system
In recent years, under vehicle surveillance and the classification of the vehicles become an indispensable task that must be achieved for security measures in certain areas such as shopping centers, government buildings, army camps etc. The main challenge to achieve this task is to monitor the under
frames of the means of transportations. In this paper, we present a novel solution to achieve this aim. Our solution consists of three main parts: monitoring, detection and classification. In the first part we design a new catadioptric camera system in which the perspective camera points downwards to the catadioptric mirror mounted to the body of a mobile robot. Thanks to the
catadioptric mirror the scenes against the camera optical axis direction can be viewed. In the second part we use speeded up robust features (SURF) in an object recognition algorithm. Fast appearance based mapping algorithm (FAB-MAP) is exploited for the classification of the means of transportations in the third
part. Proposed technique is implemented in a laboratory environment
Towards Visual Ego-motion Learning in Robots
Many model-based Visual Odometry (VO) algorithms have been proposed in the
past decade, often restricted to the type of camera optics, or the underlying
motion manifold observed. We envision robots to be able to learn and perform
these tasks, in a minimally supervised setting, as they gain more experience.
To this end, we propose a fully trainable solution to visual ego-motion
estimation for varied camera optics. We propose a visual ego-motion learning
architecture that maps observed optical flow vectors to an ego-motion density
estimate via a Mixture Density Network (MDN). By modeling the architecture as a
Conditional Variational Autoencoder (C-VAE), our model is able to provide
introspective reasoning and prediction for ego-motion induced scene-flow.
Additionally, our proposed model is especially amenable to bootstrapped
ego-motion learning in robots where the supervision in ego-motion estimation
for a particular camera sensor can be obtained from standard navigation-based
sensor fusion strategies (GPS/INS and wheel-odometry fusion). Through
experiments, we show the utility of our proposed approach in enabling the
concept of self-supervised learning for visual ego-motion estimation in
autonomous robots.Comment: Conference paper; Submitted to IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS) 2017, Vancouver CA; 8 pages, 8 figures,
2 table
Optical approach of a hypercatadioptric system depth of field
A catadioptric system is composed of a mirror and a perspective camera. Since the mirror is curved and the distance between the mirror and the camera is short, some parts of the panoramic image keep blurred. In this article, an optical approach of the panoramic system using a hyperbolic mirror is presented and its depth of field is analyzed. The impact of different parameters of mirror and camera on the quality of the panoramic image is researched and a valid method of choosing camera and mirror is presented. Finally, this article gives some possible perspectives based on these researches
Face tracking using a hyperbolic catadioptric omnidirectional system
In the first part of this paper, we present a brief review on catadioptric omnidirectional
systems. The special case of the hyperbolic omnidirectional system is analysed in depth.
The literature shows that a hyperboloidal mirror has two clear advantages over alternative
geometries. Firstly, a hyperboloidal mirror has a single projection centre [1]. Secondly, the
image resolution is uniformly distributed along the mirror’s radius [2].
In the second part of this paper we show empirical results for the detection and tracking
of faces from the omnidirectional images using Viola-Jones method. Both panoramic and
perspective projections, extracted from the omnidirectional image, were used for that purpose.
The omnidirectional image size was 480x480 pixels, in greyscale. The tracking method used
regions of interest (ROIs) set as the result of the detections of faces from a panoramic projection
of the image. In order to avoid losing or duplicating detections, the panoramic projection was
extended horizontally. Duplications were eliminated based on the ROIs established by previous
detections. After a confirmed detection, faces were tracked from perspective projections (which
are called virtual cameras), each one associated with a particular face. The zoom, pan and tilt
of each virtual camera was determined by the ROIs previously computed on the panoramic
image.
The results show that, when using a careful combination of the two projections, good frame
rates can be achieved in the task of tracking faces reliably
Efficient generic calibration method for general cameras with single centre of projection
Generic camera calibration is a non-parametric calibration technique that is applicable to any type of vision sensor. However, the standard generic calibration method was developed with the goal of generality and it is therefore sub-optimal for the common case of cameras with a single centre of projection (e.g. pinhole, fisheye, hyperboloidal catadioptric). This paper proposes novel improvements to the standard generic calibration method for central cameras that reduce its complexity, and improve its accuracy and robustness. Improvements are achieved by taking advantage of the geometric constraints resulting from a single centre of projection. Input data for the algorithm is acquired using active grids, the performance of which is characterised. A new linear estimation stage to the generic algorithm is proposed incorporating classical pinhole calibration techniques, and it is shown to be significantly more accurate than the linear estimation stage of the standard method. A linear method for pose estimation is also proposed and evaluated against the existing polynomial method. Distortion correction and motion reconstruction experiments are conducted with real data for a hyperboloidal catadioptric sensor for both the standard and proposed methods. Results show the accuracy and robustness of the proposed method to be superior to those of the standard method
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