Autonomous robots that assist humans in day to day living tasks are becoming increasingly popular. Autonomous mobile robots operate by sensing and perceiving their surrounding environment to make accurate driving decisions. A combination of several different sensors such as LiDAR, radar, ultrasound sensors and cameras are utilized to sense the surrounding environment of autonomous vehicles. These heterogeneous sensors simultaneously capture various physical attributes of the environment. Such multimodality and redundancy of sensing need to be positively utilized for reliable and consistent perception of the environment through sensor data fusion. However, these multimodal sensor data streams are different from each other in many ways, such as temporal and spatial resolution, data format, and geometric alignment. For the subsequent perception algorithms to utilize the diversity offered by multimodal sensing, the data streams need to be spatially, geometrically and temporally aligned with each other. In this paper, we address the problem of fusing the outputs of a Light Detection and Ranging (LiDAR) scanner and a wide-angle monocular image sensor for free space detection. The outputs of LiDAR scanner and the image sensor are of different spatial resolutions and need to be aligned with each other. A geometrical model is used to spatially align the two sensor outputs, followed by a Gaussian Process (GP) regression-based resolution matching algorithm to interpolate the missing data with quantifiable uncertainty. The results indicate that the proposed sensor data fusion framework significantly aids the subsequent perception steps, as illustrated by the performance improvement of a uncertainty aware free space detection algorithm.

The quality assessment of impaired stereoscopic video is a key element in designing and deploying advanced immersive media distribution platforms. A widely accepted quality metric to measure impairments of stereoscopic video is, however, still to be developed. As a step toward finding a solution to this problem, this paper proposes a full reference stereoscopic video quality metric to measure the perceptual quality of compressed stereoscopic video. A comprehensive set of subjective experiments is performed with 14 different stereoscopic video sequences, which are encoded using both the H.264 and high efficiency video coding compliant video codecs, to develop a subjective test results database of 116 test stimuli. The subjective results are analyzed using statistical techniques to uncover different patterns of subjective scoring for symmetrically and asymmetrically encoded stereoscopic video. The subjective result database is subsequently used for training and validating a simple but effective stereoscopic video quality metric considering heuristics of binocular vision. The proposed metric performs significantly better than state-of-the-art stereoscopic image and video quality metrics in predicting the subjective scores. The proposed metric and the subjective result database will be made publicly available, and it is expected that the proposed metric and the subjective assessments will have important uses in advanced 3D media delivery systems.

The use of machine learning to identify and classify offensive and defensive strategies in team sports through spatio-temporal tracking data has received significant interest recently in the literature and the global sport industry. This paper focuses on data-driven defensive strategy learning in basketball. Most research to date on basketball strategy learning has focused on offensive effectiveness and is based on the interaction between the on-ball player and principle on-ball defender, thereby ignoring the contribution of the remaining players. Furthermore, most sports analytical systems that provide play-by-play data is heavily biased towards offensive metrics such as passes, dribbles, and shots. The aim of the current study was to use machine learning to classify the different defensive strategies basketball players adopt when deviating from their initial defensive action. An analytical model was developed to recognise the one-on-one (matched) relationships of the players, which is utilised to automatically identify any change of defensive strategy. A classification model is developed based on a player and ball tracking dataset from National Basketball Association (NBA) game play to classify the adopted defensive strategy against pick-and-roll play. The methodology described is the first to analyse the defensive strategy of all in-game players (both on-ball players and off-ball players). The cross-validation results indicate that the proposed technique for automatic defensive strategy identification can achieve up to 69% accuracy of classification. Machine learning techniques, such as the one adopted here, have the potential to enable a deeper understanding of player decision making and defensive game strategies in basketball and other sports, by leveraging the player and ball tracking data.

Three-dimensional (3-D) displays provide a more realistic experience of entertainment by providing its viewers an added sensation of depth by artificially exploiting light rays to stimulate certain depth cues in the human visual system, especially binocular stereopsis. Due to its close relationship with human visual perception, mass market deployment of 3-D displays will be significantly dependant upon addressing the related perceptual factors such as visual comfort. In order to address the perceptual factors, it is very important to understand how humans experience depth on 3-D displays and how sensitive they are for different depth cues. In this paper, the sensitivity of humans for different depth cues is analyzed as applicable to 3-D viewing on stereoscopic displays. Mathematical models are derived to explain the just noticeable difference in depth (JNDD) for three different depth cues, namely binocular disparity, retinal blur, and relative size. Extensive subjective assessments are performed on a stereoscopic display with passive polarized glasses and on an auto-stereoscopic display to validate the mathematical models for JNDD. It is expected that the proposed models will have important use cases in 3-D display designing as well as 3-D content production.

Creating an accurate awareness of the environment using laser scanners is a major challenge in robotics and auto industries. LiDAR (light detection and ranging) is a powerful laser scanner that provides a detailed map of the environment. However, efficient and accurate mapping of the environment is yet to be obtained, as most modern environments contain glass, which is invisible to LiDAR. In this paper, a method to effectively detect and localise glass using LiDAR sensors is proposed. This new approach is based on the variation of range measurements between neighbouring point clouds, using a two-step filter. The first filter examines the change in the standard deviation of neighbouring clouds. The second filter uses a change in distance and intensity between neighbouring pules to refine the results from the first filter and estimate the glass profile width before updating the cartesian coordinate and range measurement by the instrument. Test results demonstrate the detection and localisation of glass and the elimination of errors caused by glass in occupancy grid maps. This novel method detects frameless glass from a long range and does not depend on intensity peak with an accuracy of 96.2%.

This paper addresses the sensitivity of human vision to spatial depth variations in a 3-D video scene, seen on a stereoscopic display, based on an experimental derivation of a just noticeable depth difference (JNDD) model. The main target is to exploit the depth perception sensitivity of humans in suppressing the unnecessary spatial depth details, hence reducing the transmission overhead allocated to depth maps. Based on the JNDD model derived, depth map sequences are preprocessed to suppress the depth details that are not perceivable by the viewers and to minimize the rendering artefacts that arise due to optical noise, where the optical noise is triggered by the inaccuracies in the depth estimation process. Theoretical and experimental evidences are provided to illustrate that the proposed depth adaptive preprocessing filter does not alter the 3-D visual quality or the view synthesis quality for free-viewpoint video applications. Experimental results suggest that the bit rate for depth map coding can be reduced up to 78% for the depth maps captured with depth-range cameras and up to 24% for the depth maps estimated with computer vision algorithms, without affecting the 3-D visual quality or the arbitrary view synthesis quality.