Effects of Ground Manifold Modeling on the Accuracy of Stixel Calculations

This paper highlights the role of ground manifold modeling for stixel calculations; stixels are medium-level data representations used for the development of computer vision modules for self-driving cars. By using single-disparity maps and simplifying ground manifold models, calculated stixels may suffer from noise, inconsistency, and false-detection rates for obstacles, especially in challenging datasets. Stixel calculations can be improved with respect to accuracy and robustness by using more adaptive ground manifold approximations. A comparative study of stixel results, obtained for different ground-manifold models (e.g., plane-fitting, line-fitting in v-disparities or polynomial approximation, and graph cut), defines the main part of this paper. This paper also considers the use of trinocular stereo vision and shows that this provides options to enhance stixel results, compared with the binocular recording. Comprehensive experiments are performed on two publicly available challenging datasets. We also use a novel way for comparing calculated stixels with ground truth. We compare depth information, as given by extracted stixels, with ground-truth depth, provided by depth measurements using a highly accurate LiDAR range sensor (as available in one of the public datasets). We evaluate the accuracy of four different ground-manifold methods. The experimental results also include quantitative evaluations of the tradeoff between accuracy and run time. As a result, the proposed trinocular recording together with graph-cut estimation of ground manifolds appears to be a recommended way, also considering challenging weather and lighting conditions

Rift Racers - Effect of Balancing and Competition on Exertion, Enjoyment, and Motivation in an Immersive Exergame

By immersing themselves in a game users may exert themselves more than they would in every day life. One important driving factor in games and many forms of exercise is competition, at once engaging socially in the activity and trying to outdo an opponent or oneself. Large differences in fitness levels make competition infeasible between some opponents, but exergaming can remedy this with the use of balancing via exertion.We developed a fully immersive virtual cycling race and balanced the competition between opponents by scaling their speed according to how close they were to their target heart rate. Incorporating a virtual reality headset and a vibrant 3D world, users were exhilarated and pushed themselves to high levels of exertion. Our results suggest that balanced games can reduce the performance gap between opponents, and might increase motivation and enjoyment for users with lower fitness level. However, heart-rate balancing might be demotivating for very fit users. </p

Precision of pose estimation using corner detection.

The aim of this research was to develop a method for recording ground truth with performance comparable to motion capture, in order to produce high-quality outdoor visual odometry datasets. A novel fiducial marker system was developed, featuring a smooth pattern which is used in an optimisation process to produce refined estimates. On average, precision was increased by 27 % compared to traditional fiducial markers. To investigate the limit of the increase in pose estimation precision possible with this method, the marker was modelled as a dense grid of checkerboard corners and the Cramér-Rao lower bound of the corresponding estimator was derived symbolically. This gave a lower bound for the variance of a pose estimated from a given image. The model was validated in simulation and using real images. The distribution of the error for a common checkerboard corner detector was evaluated to determine whether modelling it using independent and identically distributed Gaussian random variables was valid. In a series of experiments where images were collected from a tripod, a robot arm, and a slider-type electric actuator, it was determined that the error is usually normally distributed but its variance depends on the amount of lens blur in the image, and that any amount of motion blur can produce correlated results. Furthermore, in images with little blur (less than approximately one pixel) the estimates are biased, and both the bias and the variance are dependent on the location of the corner within a pixel. In real images, the standard deviation of the noise was around 80 % larger at the pixel edges than at the centre. The intensity noise from the image sensor was also found not to be identically distributed: in one camera, the standard deviation of the intensity noise varied by a factor of approximately four within the region around a checkerboard corner. This research suggests that it is possible to significantly increase fiducial marker pose estimation precision, presents a novel approach for predicting and evaluating pose estimation precision, and highlights sources of error not considered in prior work

Efficient high-resolution video compression scheme using background and foreground layers

Video coding using dynamic background frame achieves better compression compared to the traditional techniques by encoding background and foreground separately. This process reduces coding bits for the overall frame significantly; however, encoding background still requires many bits that can be compressed further for achieving better coding efficiency. The cuboid coding framework has been proven to be one of the most effective methods of image compression which exploits homogeneous pixel correlation within a frame and has better alignment with object boundary compared to traditional block-based coding. In a video sequence, the cuboid-based frame partitioning varies with the changes of the foreground. However, since the background remains static for a group of pictures, the cuboid coding exploits better spatial pixel homogeneity. In this work, the impact of cuboid coding on the background frame for high-resolution videos (Ultra-High-Definition (UHD) and 360-degree videos) is investigated using the multilayer framework of SHVC. After the cuboid partitioning, the method of coarse frame generation has been improved with a novel idea by keeping human-visual sensitive information. Unlike the traditional SHVC scheme, in the proposed method, cuboid coded background and the foreground are encoded in separate layers in an implicit manner. Simulation results show that the proposed video coding method achieves an average BD-Rate reduction of 26.69% and BD-PSNR gain of 1.51 dB against SHVC with significant encoding time reduction for both UHD and 360 videos. It also achieves an average of 13.88% BD-Rate reduction and 0.78 dB BD-PSNR gain compared to the existing relevant method proposed by X. Hoang Van. © 2013 IEEE

Is working with what we have enough?

Augmented reality (AR) digital environments have introduced a new complexity to digital investigation where augmented overlays of real objects may be momentary, changed, distorted and evade the usual methods for evidence collection. It is possible an investigator applying standard investigation methods factually reports a real situation and its digital context but has none of the relevant evidence. In this situation the potential for a fair hearing is low and the chance of retrial high. Such situations are unacceptably dangerous and require redress. In this paper the AR condition is considered in terms of its complexity and management during an investigation. The most important issue is awareness and the investigator factoring in the potential for augmentation in any investigation