We present a novel system for detection, localization and tracking of multiple people, which fuses a multi-view computer vision approach with a radio-based localization system. The proposed fusion combines the best of both worlds, excellent computer-vision-based localization, and strong identity information provided by the radio system, and is therefore able to perform tracking by identification, which makes it impervious to propagated identity switches. We present comprehensive methodology for evaluation of systems that perform person localization in world coordinate system and use it to evaluate the proposed system as well as its components. Experimental results on a challenging indoor dataset, which involves multiple people walking around a realistically cluttered room, confirm that proposed fusion of both systems significantly outperforms its individual components. Compared to the radio-based system, it achieves better localization results, while at the same time it successfully prevents propagation of identity switches that occur in pure computer-vision-based tracking.

We present a new semantic segmentation algorithm for obstacle detection in unmanned surface vehicles. The novelty lies in the graphical model that incorporates boat tilt measurements from the on-board inertial measurement unit (IMU). The IMU readings are used to estimate the location of horizon line in the image, and automatically adjusts the priors in the probabilistic semantic segmentation algorithm. We derive the necessary horizon projection equations, an efficient optimization algorithm for the proposed graphical model, and a practical IMU-camera-USV calibration. A new challenging dataset, which is the largest multi-sensor dataset of its kind, is constructed. Results show that the proposed algorithm significantly outperforms state of the art, with 32% improvement in water-edge detection accuracy, an over 15 % reduction of false positive rate, an over 70 % reduction of false negative rate, and an over 55 % increase of true positive rate, while running in real-time on a single core in Matlab.

Situation awareness (SA) refers to the awareness of all relevant sources of information, an ability to synthesise this information using domain knowledge gained from past experiences and the ability to physically respond to a situation. Expert-novice differences have been widely reported in decision-making in complex situations although determining the small differences in expert behaviour are more elusive. This study considered how expert squash players use SA to decide on what shot to play. Matches at the 2010 (n = 14) and 2011 (n = 27) Rowe British Grand Prix were recorded and processed using Tracker software. Shot type, ball location, players’ positions on court and movement parameters between the time an opponent played a shot prior to the player’s shot to the time of the opponent’s following shot were captured 25 times per second. Six SA clusters were named to relate to the outcome of a shot ranging from a defensive shot played under pressure to create time to an attempted winner played under no pressure with the opponent out of position. This new methodology found fine-grained SA differences in expert behaviour, even for the same shot type played from the same court area, beyond the usual expert-novice differences.