Using reflective clothing to enhance the conspicuity of bicyclists at night

Bicycling at night is more dangerous than in the daytime and poor conspicuity is likely to be a contributing factor. The use of reflective markings on a pedestrian’s major joints to facilitate the perception of biological motion has been shown to greatly enhance pedestrian conspicuity at night, but few corresponding data exist for bicyclists. Twelve younger and twelve older participants drove around a closed-road circuit at night and indicated when they first saw a bicyclist who wore black clothing either alone, or together with a reflective bicycling vest, or a vest plus ankle and knee reflectors. The bicyclist pedaled in place on a bicycle that had either a static or flashing light, or no light on the handlebars. Bicyclist clothing significantly affected conspicuity; drivers responded to bicyclists wearing the vest plus ankle and knee reflectors at significantly longer distances than when the bicyclist wore the vest alone or black clothing without a vest. Older drivers responded to bicyclists less often and at shorter distances than younger drivers. The presence of a bicycle light, whether static or flashing, did not enhance the conspicuity of the bicyclist; this may result in bicyclists who use a bicycle light being overconfident of their own conspicuity at night. The implications of our findings are that ankle and knee markings are a simple and very effective approach for enhancing bicyclist conspicuity at night.

Demonstration of closed-­loop airborne sense-­and-­avoid using machine vision

This paper describes a vision-based airborne collision avoidance system developed by the Australian Research Centre for Aerospace Automation (ARCAA) under its Dynamic Sense-and-Act (DSA) program. We outline the system architecture and the flight testing undertaken to validate the system performance under realistic collision course scenarios. The proposed system could be implemented in either manned or unmanned aircraft, and represents a step forward in the development of a “sense-and-avoid” capability equivalent to human “see-and-avoid”.

Machine vision toolbox for MATLAB, release 3 [Software]

The Toolbox, combined with MATLAB ® and a modern workstation computer, is a useful and convenient environment for investigation of machine vision algorithms. For modest image sizes the processing rate can be sufficiently ``real-time'' to allow for closed-loop control. Focus of attention methods such as dynamic windowing (not provided) can be used to increase the processing rate. With input from a firewire or web camera (support provided) and output to a robot (not provided) it would be possible to implement a visual servo system entirely in MATLAB. Provides many functions that are useful in machine vision and vision-based control. Useful for photometry, photogrammetry, colorimetry. It includes over 100 functions spanning operations such as image file reading and writing, acquisition, display, filtering, blob, point and line feature extraction, mathematical morphology, homographies, visual Jacobians, camera calibration and color space conversion.

Even moderate visual impairments degrade drivers’ ability to see pedestrians at night

Purpose: To determine the effect of moderate levels of refractive blur and simulated cataracts on nighttime pedestrian conspicuity in the presence and absence of headlamp glare. Methods: The ability to recognize pedestrians at night was measured in 28 young adults (M=27.6 years) under three visual conditions: normal vision, refractive blur and simulated cataracts; mean acuity was 20/40 or better in all conditions. Pedestrian recognition distances were recorded while participants drove an instrumented vehicle along a closed road course at night. Pedestrians wore one of three clothing conditions and oncoming headlamps were present for 16 participants and absent for 12 participants. Results: Simulated visual impairment and glare significantly reduced the frequency with which drivers recognized pedestrians and the distance at which the drivers first recognized them. Simulated cataracts were significantly more disruptive than blur even though photopic visual acuity levels were matched. With normal vision, drivers responded to pedestrians at 3.6x and 5.5x longer distances on average than for the blur or cataract conditions, respectively. Even in the presence of visual impairment and glare, pedestrians were recognized more often and at longer distances when they wore a “biological motion” reflective clothing configuration than when they wore a reflective vest or black clothing. Conclusions: Drivers’ ability to recognize pedestrians at night is degraded by common visual impairments even when the drivers’ mean visual acuity meets licensing requirements. To maximize drivers’ ability to see pedestrians, drivers should wear their optimum optical correction, and cataract surgery should be performed early enough to avoid potentially dangerous reductions in visual performance.

Experimental validation of an unpowered unmanned aerial system : application to forced landing scenarios

The ability to perform autonomous emergency (forced) landings is one of the key technology enablers identified for UAS. This paper presents the flight test results of forced landings involving a UAS, in a controlled environment, and which was conducted to ascertain the performances of previously developed (and published) path planning and guidance algorithms. These novel 3-D nonlinear algorithms have been designed to control the vehicle in both the lateral and longitudinal planes of motion. These algorithms have hitherto been verified in simulation. A modified Boomerang 60 RC aircraft is used as the flight test platform, with associated onboard and ground support equipment sourced Off-the-Shelf or developed in-house at the Australian Research Centre for Aerospace Automation(ARCAA). HITL simulations were conducted prior to the flight tests and displayed good landing performance, however, due to certain identified interfacing errors, the flight results differed from that obtained in simulation. This paper details the lessons learnt and presents a plausible solution for the way forward.

Flight trial of an electro-optical sense-and-avoid system

This paper presents the flight trials of an electro-optical (EO) sense-and-avoid system onboard a Cessna host aircraft (camera aircraft). We focus on the autonomous collision avoidance capability of the sense-and-avoid system; that is, closed-loop integration with the onboard aircraft autopilot. We also discuss the system’s approach to target detection and avoidance control, as well as the methodology of the flight trials. The results demonstrate the ability of the sense-and-avoid system to automatically detect potential conflicting aircraft and engage the host Cessna autopilot to perform an avoidance manoeuvre, all without any human intervention

See and avoid using on-board computer vision

The integration of unmanned aircraft into civil airspace is a complex issue. One key question is whether unmanned aircraft can operate just as safely as their manned counterparts. The absence of a human pilot in unmanned aircraft automatically points to a deficiency that is the lack of an inherent see-and-avoid capability. To date, regulators have mandated that an “equivalent level of safety” be demonstrated before UAVs are permitted to routinely operate in civil airspace. This chapter proposes techniques, methods, and hardware integrations that describe a “sense-and-avoid” system designed to address the lack of a see-and-avoid capability in UAVs.

Enterprise systems curricula for Asia-Pacific region : a vision for 2020

Enterprise Systems (ES) have emerged as possibly the most important and challenging development in the corporate use of information technology in the last decade. Organizations have invested heavily in these large, integrated application software suites expecting improvments in; business processes, management of expenditure, customer service, and more generally, competitiveness, improved access to better information/knowledge (i.e., business intelligence and analytics). Forrester survey data consistently shows that investment in ES and enterprise applications in general remains the top IT spending priority, with the ES market estimated at $38 billion and predicted to grow at a steady rate of 6.9%, reaching $50 billion by 2012 (Wang & Hamerman, 2008). Yet, organizations have failed to realize all the anticipated benefits. One of the key reasons is the inability of employees to properly utilize the capabilities of the enterprise systems to complete the work and extract information critical to decision making. In response, universities (tertiary institutes) have developed academic programs aimed at addressing the skill gaps. In parallel with the proliferation of ES, there has been growing recognition of the importance of Teaching Enterprise Systems at tertiary education institutes. Many academic papers have discused the important role of Enterprise System curricula at tertiary education institutes (Ask, 2008; Hawking, 2004; Stewart, 2001), where the teaching philosophises, teaching approaches and challenges in Enterprise Systems education were discussed. Following the global trends, tertiary institutes in the Pacific-Asian region commenced introducing Enterprise System curricula in late 1990s with a range of subjects (a subject represents a single unit, rather than a collection of units; which we refer to as a course) in faculties / schools / departments of Information Technology, Business and in some cases in Engineering. Many tertiary educations commenced their initial subject offers around four salient concepts of Enterprise Systems: (1) Enterprise Systems implementations, (2) Introductions to core modules of Enterprise Systems, (3) Application customization using a programming language (e.g. ABAP) and (4) Systems Administration. While universities have come a long way in developing curricula in the enterprise system area, many obstacles remain: high cost of technology, qualified faculty to teach, lack of teaching materials, etc.

Integrated vision-based attitude estimation

For many years, computer vision has lured researchers with promises of a low-cost, passive, lightweight and information-rich sensor suitable for navigation purposes. The prime difficulty in vision-based navigation is that the navigation solution will continually drift with time unless external information is available, whether it be cues from the appearance of the scene, a map of features (whether built online or known a priori), or from an externally-referenced sensor. It is not merely position that is of interest in the navigation problem. Attitude (i.e. the angular orientation of a body with respect to a reference frame) is integral to a visionbased navigation solution and is often of interest in its own right (e.g. flight control). This thesis examines vision-based attitude estimation in an aerospace environment, and two methods are proposed for constraining drift in the attitude solution; one through a novel integration of optical flow and the detection of the sky horizon, and the other through a loosely-coupled integration of Visual Odometry and GPS position measurements. In the first method, roll angle, pitch angle and the three aircraft body rates are recovered though a novel method of tracking the horizon over time and integrating the horizonderived attitude information with optical flow. An image processing front-end is used to select several candidate lines in a image that may or may not correspond to the true horizon, and the optical flow is calculated for each candidate line. Using an Extended Kalman Filter (EKF), the previously estimated aircraft state is propagated using a motion model and a candidate horizon line is associated using a statistical test based on the optical flow measurements and location of the horizon in the image. Once associated, the selected horizon line, along with the associated optical flow, is used as a measurement to the EKF. To evaluate the accuracy of the algorithm, two flights were conducted, one using a highly dynamic Uninhabited Airborne Vehicle (UAV) in clear flight conditions and the other in a human-piloted Cessna 172 in conditions where the horizon was partially obscured by terrain, haze and smoke. The UAV flight resulted in pitch and roll error standard deviations of 0.42° and 0.71° respectively when compared with a truth attitude source. The Cessna 172 flight resulted in pitch and roll error standard deviations of 1.79° and 1.75° respectively. In the second method for estimating attitude, a novel integrated GPS/Visual Odometry (GPS/VO) navigation filter is proposed, using a structure similar to a classic looselycoupled GPS/INS error-state navigation filter. Under such an arrangement, the error dynamics of the system are derived and a Kalman Filter is developed for estimating the errors in position and attitude. Through similar analysis to the GPS/INS problem, it is shown that the proposed filter is capable of recovering the complete attitude (i.e. pitch, roll and yaw) of the platform when subjected to acceleration not parallel to velocity for both the monocular and stereo variants of the filter. Furthermore, it is shown that under general straight line motion (e.g. constant velocity), only the component of attitude in the direction of motion is unobservable. Numerical simulations are performed to demonstrate the observability properties of the GPS/VO filter in both the monocular and stereo camera configurations. Furthermore, the proposed filter is tested on imagery collected using a Cessna 172 to demonstrate the observability properties on real-world data. The proposed GPS/VO filter does not require additional restrictions or assumptions such as platform-specific dynamics, map-matching, feature-tracking, visual loop-closing, gravity vector or additional sensors such as an IMU or magnetic compass. Since no platformspecific dynamics are required, the proposed filter is not limited to the aerospace domain and has the potential to be deployed in other platforms such as ground robots or mobile phones.

Research initiatives to improve the visibility and hence safety of road workers at night-time

Research has demonstrated the benefits that clothing incorporating retroreflective markers can provide in significantly improving visibility and reducing accidents, especially at night. Adding biomotion markings to standard vests can enhance the night-time conspicuity of roadway workers by capitalizing on perceptual capabilities.

Large scale monocular vision-only mapping from a fixed-wing sUAS

This paper presents the application of a monocular visual SLAMon a fixed-wing small Unmanned Aerial System (sUAS) capable of simultaneous estimation of aircraft pose and scene structure. We demonstrate the robustness of unconstrained vision alone in producing reliable pose estimates of a sUAS, at altitude. It is ultimately capable of online state estimation feedback for aircraft control and next-best-view estimation for complete map coverage without the use of additional sensors.We explore some of the challenges of visual SLAM from a sUAS including dealing with planar structure, distant scenes and noisy observations. The developed techniques are applied on vision data gathered from a fast-moving fixed-wing radio control aircraft flown over a 1×1km rural area at an altitude of 20-100m.We present both raw Structure from Motion results and a SLAM solution that includes FAB-MAP based loop-closures and graph-optimised pose. Timing information is also presented to demonstrate near online capabilities. We compare the accuracy of the 6-DOF pose estimates to an off-the-shelfGPS aided INS over a 1.7kmtrajectory.We also present output 3D reconstructions of the observed scene structure and texture that demonstrates future applications in autonomous monitoring and surveying.

Real-time power line extraction from unmanned aerial system video images

In this paper a real-time vision based power line extraction solution is investigated for active UAV guidance. The line extraction algorithm starts from ridge points detected by steerable filters. A collinear line segments fitting algorithm is followed up by considering global and local information together with multiple collinear measurements. GPU boosted algorithm implementation is also investigated in the experiment. The experimental result shows that the proposed algorithm outperforms two baseline line detection algorithms and is able to fitting long collinear line segments. The low computational cost of the algorithm make suitable for real-time applications.

Image Region Salience for Improving Appearance-Based Place Recognition using a Supervised Classifier System

Many state of the art vision-based Simultaneous Localisation And Mapping (SLAM) and place recognition systems compute the salience of visual features in their environment. As computing salience can be problematic in radically changing environments new low resolution feature-less systems have been introduced, such as SeqSLAM, all of which consider the whole image. In this paper, we implement a supervised classifier system (UCS) to learn the salience of image regions for place recognition by feature-less systems. SeqSLAM only slightly benefits from the results of training, on the challenging real world Eynsham dataset, as it already appears to filter less useful regions of a panoramic image. However, when recognition is limited to specific image regions performance improves by more than an order of magnitude by utilising the learnt image region saliency. We then investigate whether the region salience generated from the Eynsham dataset generalizes to another car-based dataset using a perspective camera. The results suggest the general applicability of an image region salience mask for optimizing route-based navigation applications.

Airborne collision scenario flight tests : impact of angle measurement errors on reactive vision-based avoidance control

The future emergence of many types of airborne vehicles and unpiloted aircraft in the national airspace means collision avoidance is of primary concern in an uncooperative airspace environment. The ability to replicate a pilot’s see and avoid capability using cameras coupled with vision based avoidance control is an important part of an overall collision avoidance strategy. But unfortunately without range collision avoidance has no direct way to guarantee a level of safety. Collision scenario flight tests with two aircraft and a monocular camera threat detection and tracking system were used to study the accuracy of image-derived angle measurements. The effect of image-derived angle errors on reactive vision-based avoidance performance was then studied by simulation. The results show that whilst large angle measurement errors can significantly affect minimum ranging characteristics across a variety of initial conditions and closing speeds, the minimum range is always bounded and a collision never occurs.