Splat Size And Reconstruction Kernel For Artefact Reduction In Rendering

This research is motivated towards understanding the relationship between the choice of splat size and reconstruction kernel and splatting artefacts in deferred splatting, devising new methods on determining splat size and reconstruction kernel to overcome splattng artefacts, and improve the visual quality of deferred splatting such that it is comparable to the visual quality of triangle rendering without compromising the performance of deferred splatting significantly

Automated Tactical Analysis of Broadcast Badminton Match Videos

In elite badminton, tactics analysis studies playing patterns to help players identify tactical strengths and weaknesses and gain competitive advantage. Tactical analysis involves many laborious tasks (e.g. record match videos, annotate data from videos, match analysis, etc.). Abundance of online broadcast badminton match videos spurs interest in automated tactical analysis

BadmintonDB: A Badminton Dataset for Player-specific Match Analysis and Prediction

This paper introduces BadmintonDB, a new badminton dataset for training models for player-specific match analysis and prediction tasks, which are interesting challenges. The dataset features rally, strokes, and outcome annotations of 9 real-world badminton matches between two top players. We discussed our methodologies and processes behind selecting and annotating the matches. We also proposed player-independent and player-dependent Naive Bayes baselines for rally outcome prediction. The paper concludes with the analysis performed on the experiments to study the effects of player-dependent model on the prediction performances. We released our dataset at https://github.com/kwban/badminton-db

Improved Canny Edges Using Ant Colony Optimization

Ant colony optimization (ACO) is a metaheuristic approach for solving hard optimization problem. It has been applied to solve various image processing problems such as image segmentation, classification, image analysis and edge detection. In this paper, we present an Improved Canny edges (ICE-ACO) algorithm which uses ACO to solve the problem of linking disjointed edges produced by Canny edge detector

Development of Realistic Hazard Scenarios in a Malaysian Driving Simulator for Education

In this paper, we present the development of a driving education prototype simulator, aiming to address dangerous traffic situations frequently encountered in daily life, particularly in urban areas like Klang Valley, Malaysia. Our initial driving simulator version focused on track-based training tailored to Malaysian driving exams, but we recognized its limitations in preparing learner drivers for real-world challenges due to the lack of risk. To add the value of study to our project, we designed three scenarios based on common dangerous situations observed in dashboard camera (dashcam) footage from Malaysian drivers. Due to the lack of reliable openly discussed methods on designing such scenarios and implementing it into a virtual environment, we devised a rudimentary method in the scenario designs. Our goal here is to open up the discussion to study reliable methods of designing scenarios not just for driving education but all forms of scenarios that are used for training environments. These scenarios have the potential to benefit education and training by creating realistic scenarios and environments

Exploration and Showcase of FSMs and HFSMs Traffic in Low-Cost Educational Driving Simulator

Finite state machine (FSM) is a model of computation that executes an exact finite number of states at any given time where Hierarchical Finite State Machines (HFSM) can group multiple FSMs and execute as one state. Given these techniques can be used to exclusively execute certain states, it is widely used in games. In this paper we will explore and showcase the implementation of FSMs and HFSMs to create traffic non-playing characters (NPC) in our low-cost educational driving simulators. We will also discuss possible techniques that can be use. It is also in the interest to push for more understanding in any gap for traffic NPCs in similar low-cost educational driving sims in the future

Modelling Traffic-Based Driving Simulator Using Artificial Intelligence and Virtual Reality Techniques

With more cars on the roads of Malaysia and the gaining popularity of simulators used in training for various skills. It is only logical to fill in the gap where no case studies were done using simulators curated for Malaysians with Malaysian specific traffic conditions and curriculum to increase learning effectiveness for driving education. With a pilot study done, we seek to further investigate by using different technologies in order to enhance learning effectiveness

Enhancement of Prototype Driving Simulator Using Available AI-Based Game Technology

Simulators—games that simulate the real world in a virtual environment, such as racing simulators, have been widely studied and documented. Their uses could, however, be further expanded into the field of driving education. The motivations behind this study are to dive into the trend of immersive learning and exploit artificial intelligence (AI)-based game technology to benefit driving education. The preliminary work shows that the use of a driving simulator as a teaching tool for driving education has improved the passing rate of driving training. This paper proposes several enhancements to the prototype driving simulator, by utilizing rudimentary AIs to simulate basic traffic for certain scenarios. The driving simulator is developed in Unity and is paired with a driving rig consisting of a steering wheel and pedals. The project currently is a functioning driving simulator that can accept both controller and steering wheel inputs. For prototype enhancement, three scenario-based tracks and a mock-up town with a traffic light system and AI traffic were added. The prototype now includes five circuit tracks and two on-the-road tracks based on the Standard Examination syllabus, three scenario-based tracks, and a mock-up town with AI traffic, together with an automated test mode for the syllabus tracks. The free practice mode will be available for all tracks

Modelling and Evaluation of Driving Simulator for Driving Education in Malaysia

Driving simulator has been widely used as one of driver training tools because it provides a safe environment which does not expose drivers to hazards. However, Malaysia has yet to adopt the driving simulator in the driving course. In this paper, a cost effective and modular driving simulator prototype integrated is designed and developed based on the Malaysian Ministry of Transport’s Standardised License Test. Seven modules which correspond to five practical syllabus circuit tracks and two on-the-road theories are created using a real time development tool named “Unity” and integrated with some off-the-shelf hardware namely a steering wheel, gear shifter and pedals. The justification of the simulator is confirmed by conducting a unique experimental procedure on it participated by 26 individuals. They are divided into two groups each of which follows two different training methodology before taking part in the simulator test mode. One group is provided with only printed materials and another group is allowed to practise in the simulator. Experimental results show that the transfer of skills is far better among the participants of the group who are allowed to practise the simulator before taking part in the automated test of the simulator