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

ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing

We address the problem of finding realistic geometric corrections to a foreground object such that it appears natural when composited into a background image. To achieve this, we propose a novel Generative Adversarial Network (GAN) architecture that utilizes Spatial Transformer Networks (STNs) as the generator, which we call Spatial Transformer GANs (ST-GANs). ST-GANs seek image realism by operating in the geometric warp parameter space. In particular, we exploit an iterative STN warping scheme and propose a sequential training strategy that achieves better results compared to naive training of a single generator. One of the key advantages of ST-GAN is its applicability to high-resolution images indirectly since the predicted warp parameters are transferable between reference frames. We demonstrate our approach in two applications: (1) visualizing how indoor furniture (e.g. from product images) might be perceived in a room, (2) hallucinating how accessories like glasses would look when matched with real portraits.Comment: Accepted to CVPR 2018 (website & code: https://chenhsuanlin.bitbucket.io/spatial-transformer-GAN/

Real Time UAV Altitude, Attitude and Motion Estimation form Hybrid Stereovision

International audienceKnowledge of altitude, attitude and motion is essential for an Unmanned Aerial Vehicle during crit- ical maneuvers such as landing and take-off. In this paper we present a hybrid stereoscopic rig composed of a fisheye and a perspective camera for vision-based navigation. In contrast to classical stereoscopic systems based on feature matching, we propose methods which avoid matching between hybrid views. A plane-sweeping approach is proposed for estimating altitude and de- tecting the ground plane. Rotation and translation are then estimated by decoupling: the fisheye camera con- tributes to evaluating attitude, while the perspective camera contributes to estimating the scale of the trans- lation. The motion can be estimated robustly at the scale, thanks to the knowledge of the altitude. We propose a robust, real-time, accurate, exclusively vision-based approach with an embedded C++ implementation. Although this approach removes the need for any non-visual sensors, it can also be coupled with an Inertial Measurement Unit

Planar Homography Estimation from Traffic Streams via Energy Functional Minimization

The 3x3 homography matrix specifies the mapping between two images of the same plane as viewed by a pinhole camera. Knowledge of the matrix allows one to remove the perspective distortion and apply any similarity transform, effectively making possible the measurement of distances and angles on the image. A rectified road scene for instance, where vehicles can be segmented and tracked, gives rise to ready estimates of their velocities and spacing or categorization of their type. Typical road scenes render the classical approach to homography estimation difficult. The Direct Linear Transform is highly susceptible to noise and usually requires refining via an further nonlinear penalty minimization. Additionally, the penalty is a function of the displacement between measured and calibrated coordinates, a quantity unavailable in a scene for which we have no knowledge of the road coordinates. We propose instead to achieve metric rectification via the minimization of an energy that measures the violation of two constraints: the divergence-free nature of the traffic flow and the orthogonality of the flow and transverse directions under the true transform. Given that an homography is only determined up to scale, the minimization is performed on the Lie group $SL(3)$, for which we develop a gradient descent algorithm. While easily expressed in the world frame, the energy must be computed from measurements made in the image and thus must be pulled back using standard differential geometric machinery to the image frame. We develop an enhancement to the algorithm by incorporating optical flow ideas and apply it to both a noiseless test case and a suite of real-world video streams to demonstrate its efficacy and convergence. Finally, we discuss the extension to a 3D-to-planar mapping for vehicle height inference and an homography that is allowed to vary over the image, invoking a minimization on Diff$(SL(3))$

Homography-based Tracking for Central Catadioptric Cameras

This paper presents a parametric approach for tracking piecewise planar scenes with central catadioptric cameras (including perspective cameras). We extend the standard notion of homography to this wider range of devices through the unified projection model on the sphere. We avoid unwarping the image to a perspective view and take into account the non-uniform pixel resolution specific to non-perspective central catadioptric sensors. The homography is parametrised by the Lie algebra of the special linear group SL(3) to ensure that only eight free parameters are estimated. With this model, we use an efficient second-order minimisation technique leading to a fast tracking algorithm with a complexity similar to a first-order approach. The developed algorithm was tested on the estimation of the displacement of a mobile robot in a real application and proved to be very precise