David Van Hamme

Cycling around a Curve: The Effect of Cycling Speed on Steering and Gaze Behavior

Although it is generally accepted that visual information guides steering, it is still unclear whether a curvature matching strategy or a ‘look where you are going’ strategy is used while steering through a curved road. The current experiment investigated to what extent the existing models for curve driving also apply to cycling around a curve, and tested the influence of cycling speed on steering and gaze behavior. Twenty-five participants were asked to cycle through a semicircular lane three consecutive times at three different speeds while staying in the center of the lane. The observed steering behavior suggests that an anticipatory steering strategy was used at curve entrance and a compensatory strategy was used to steer through the actual bend of the curve. A shift of gaze from the center to the inside edge of the lane indicates that at low cycling speed, the ‘look where you are going’ strategy was preferred, while at higher cycling speeds participants seemed to prefer the curvature matching strategy. Authors suggest that visual information from both steering strategies contributes to the steering system and can be used in a flexible way. Based on a familiarization effect, it can be assumed that steering is not only guided by vision but that a short-term learning component should also be taken into account.

C-EFIC: Color and Edge Based Foreground Background Segmentation with Interior Classification

The detection of foreground regions in video streams is an essential part of many computer vision algorithms. Considerable contributions were made to this field over the past years. However, varying illumination circumstances and changing camera viewpoints provide major challenges for all available algorithms. In this paper, a robust foreground background segmentation algorithm is proposed. Both Local Ternary Pattern based edge descriptors and RGB color information are used to classify individual pixels. Furthermore, camera viewpoints are detected and compensated for. We will show that this algorithm is able to handle challenging conditions and achieves state-of-the-art results on the comprehensive ChangeDetection.NET 2014 dataset.

A Visual SLAM System on Mobile Robot Supporting Localization Services to Visually Impaired People

This paper describes a Visual SLAM system developed on a mobile robot in order to support localization services to visually impaired people. The proposed system aims to provide services in small or mid-scale environments such as inside a building or campus of school where conventional positioning data such as GPS, WIFI signals are often not available. Toward this end, we adapt and improve existing vision-based techniques in order to handle issues in the indoor environments. We firstly design an image acquisition system to collect visual data. On one hand, a robust visual odometry method is adjusted to precisely create the routes in the environment. On the other hand, we utilize the Fast-Appearance Based Mapping algorithm that is may be the most successful for matching places in large scenarios. In order to better estimate robot’s location, we utilize a Kalman Filter that combines the matching results of current observation and the estimation of robot states based on its kinematic model. The experimental results confirmed that the proposed system is feasible to navigate the visually impaired people in the indoor environments.

Fire Detection in Color Images Using Markov Random Fields

Automatic video-based fire detection can greatly reduce fire alert delay in large industrial and commercial sites, at a minimal cost, by using the existing CCTV camera network. Most traditional computer vision methods for fire detection model the temporal dynamics of the flames, in conjunction with simple color filtering. An important drawback of these methods is that their performance degrades at lower framerates, and they cannot be applied to still images, limiting their applicability. Also, real-time operation often requires significant computational resources, which may be unfeasible for large camera networks. This paper presents a novel method for fire detection in static images, based on a Markov Random Field but with a novel potential function. The method detects 99.6% of fires in a large collection of test images, while generating less false positives then a state-of-the-art reference method. Additionally, parameters are easily trained on a 12-image training set with minimal user input.

Edge based foreground background estimation with interior/exterior classification

Foreground background estimation is an essential task in many video analysis applications. Considerable improvements are still possible, especially concerning light condition invariance. In this paper, we propose a novel algorithm which attends to this requirement. We use modified Local Ternary Pattern (LTP) descriptors to find likely strong and stable “foreground gradient” locations. The proposed algorithm then classifies pixels as interior or exterior, using a shortest path algorithm, which proves to be robust against contour gaps.

Lane identification based on robust visual odometry

Many important intelligent vehicle systems rely on lane-level position estimates. We propose a novel lane identification approach based on robust monocular visual odometry. The images obtained from the visual odometry camera as well as the trajectory estimate are used to construct a linearized representation of the world plane near the vehicle trajectory. This linear section is classified into road surface and non-road surface using a Gaussian Mixture Model. The width of the available road surface on either side is measured to detect extra drivable lanes. Coupled with road map annotations describing the number of lanes, this allows to determine the lane index of the vehicle. Preliminary experiments on a test set of 32 segments of 120m each prove the viability of the method.

Robust Matching of Occupancy Maps for Odometry in Autonomous Vehicles

In this paper we propose a novel real-time method for SLAM in autonomous vehicles. The environment is mapped using a probabilistic occupancy map model and EGO motion is estimated within the same environment by using a feedback loop. Thus, we simplify the pose estimation from 6 to 3 degrees of freedom which greatly impacts the robustness and accuracy of the system. Input data is provided via a rotating laser scanner as 3D measurements of the current environment which are projected on the ground plane. The local ground plane is estimated in real-time from the actual point cloud data using a robust plane fitting scheme based on the RANSAC principle. Then the computed occupancy map is registered against the previous map using phase correlation in order to estimate the translation and rotation of the vehicle. Experimental results demonstrate that the method produces high quality occupancy maps and the measured translation and rotation errors of the trajectories are lower compared to other 6DOF methods. The entire SLAM system runs on a mid-range GPU and keeps up with the data from the sensor which enables more computational power for the other tasks of the autonomous vehicle.

Cell-Based Approach for 3D Reconstruction from Incomplete Silhouettes

Shape-from-silhouettes is a widely adopted approach to compute accurate 3D reconstructions of people or objects in a multi-camera environment. However, such algorithms are traditionally very sensitive to errors in the silhouettes due to imperfect foreground-background estimation or occluding objects appearing in front of the object of interest. We propose a novel algorithm that is able to still provide high quality reconstruction from incomplete silhouettes. At the core of the method is the partitioning of reconstruction space in cells, i.e. regions with uniform camera and silhouette coverage properties. A set of rules is proposed to iteratively add cells to the reconstruction based on their potential to explain discrepancies between silhouettes in different cameras. Experimental analysis shows significantly improved F1-scores over standard leave-M-out reconstruction techniques.

C-EFIC: Color and edge based foreground background segmentation with interior classification

The detection of foreground regions in video streams is an essential part of many computer vision algorithms. Considerable contributions were made to this field over the past years. However, varying illumination circumstances and changing camera viewpoints provide major challenges for all available algorithms. In this paper, a robust foreground background segmentation algorithm is proposed. Both Local Ternary Pattern based edge descriptors and RGB color information are used to classify individual pixels. Furthermore, camera viewpoints are detected and compensated for. We will show that this algorithm is able to handle challenging conditions and achieves state-of-the-art results on the comprehensive ChangeDetection.NET 2014 dataset.

Cycling around bends: the effect of cycling speed on steering and gaze behavior

Although it is generally accepted that visual information guides steering, there is no consensus whether the tangent point strategy (the point of the inner lane boundary bearing the highest curvature in the 2D retinal image) or the gaze sampling strategy (looking at a points in the future path) is best suited to guide steering around bends. Unfortunately, visual behavior while negotiating curves has almost uniquely been tested in car driving situations and no effect of driving speed has been described yet. Therefore, current research investigates the effect of cycling speed on the visual behavior while cycling curves.