15,614 research outputs found
INTELLIGENT COMPUTER VISION SYSTEM FOR SCORE DETECTION IN BASKETBALL
Development of an intelligent computer vision system for Smart IoT basketball training and entertainment includes the development of a range of various subsystems, where score detection subsystem is playing a crucial role. This paper proposes the architecture of such a score detection subsystem to improve reliability and accuracy of the RFID technology used primarily for verification purposes. Challenges encompass both hardware-software interdependencies, optimal camera selection, and cost-effectiveness considerations. Leveraging machine learning algorithms, the vision-based subsystem aims not only to detect scores but also to facilitate online video streaming. Although the use of multiple cameras offers expanded field coverage and heightened precision, it concurrently introduces technical intricacies and increased costs due to image fusion and escalated processing requirements. This research navigates the intricate balance between achieving precise score detection and pragmatic system development. Through precise camera configuration optimization, the proposed system harmonizes hardware and software components
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
Tactical decision making for autonomous driving is challenging due to the
diversity of environments, the uncertainty in the sensor information, and the
complex interaction with other road users. This paper introduces a general
framework for tactical decision making, which combines the concepts of planning
and learning, in the form of Monte Carlo tree search and deep reinforcement
learning. The method is based on the AlphaGo Zero algorithm, which is extended
to a domain with a continuous state space where self-play cannot be used. The
framework is applied to two different highway driving cases in a simulated
environment and it is shown to perform better than a commonly used baseline
method. The strength of combining planning and learning is also illustrated by
a comparison to using the Monte Carlo tree search or the neural network policy
separately
The Tracer Method: Don\u27t Blink or You Might Miss it. A Novel Methodology Combining Cognitive Task Analysis and Eye Tracking
This thesis describes the development and first demonstration of a new Human Factors method, The Tracer Method, which is a combination of Cognitive Task Analysis (CTA) and Eye Tracking. The study evaluated whether the two methods together produce new and different information than either method alone could provide. The method was tested using a video game, Overwatch, a dynamic, complex, and multiplayer game. The evaluation included: 1. Examining both in the same context (game), 2. Establishing unique contributions of each method alone, and 3. Evaluating overlapping information. Results identified some overlap between the two methods that provided some cross-validation of the data. Cognitive Task Analysis provided higher level strategies and course of actions that players implement during their games, while eye tracking provided visual patterns of search (order of eye movements). However, when combined, the two methods provide strategy information in context that neither method alone can provide. CTA elicits insight into how individuals make decisions and apply previous knowledge, experience, and environmental information. Eye tracking can support this through predictive models of individual’s eye tracking, to understand which elements are utilized in making predictions and situational assessments. We provide a tutorial and insight into best practices for implementation of The Tracer Method. This is the initial development of the new method, and on-going research is validating it in different environments. The Tracer Method is the first combined and documented systematic methodology that utilizes a changing and complicated environment and tests the interaction and output of Critical Decision Method and Eye Tracking
Influence map-based pathfinding algorithms in video games
Path search algorithms, i.e., pathfinding algorithms, are used to solve shortest path problems
by intelligent agents, ranging from computer games and applications to robotics. Pathfinding
is a particular kind of search, in which the objective is to find a path between two nodes. A
node is a point in space where an intelligent agent can travel. Moving agents in physical or
virtual worlds is a key part of the simulation of intelligent behavior. If a game agent is not able
to navigate through its surrounding environment without avoiding obstacles, it does not seem
intelligent. Hence the reason why pathfinding is among the core tasks of AI in computer games.
Pathfinding algorithms work well with single agents navigating through an environment. In realtime
strategy (RTS) games, potential fields (PF) are used for multi-agent navigation in large
and dynamic game environments. On the contrary, influence maps are not used in pathfinding.
Influence maps are a spatial reasoning technique that helps bots and players to take decisions
about the course of the game. Influence map represent game information, e.g., events and
faction power distribution, and is ultimately used to provide game agents knowledge to take
strategic or tactical decisions. Strategic decisions are based on achieving an overall goal, e.g.,
capture an enemy location and win the game. Tactical decisions are based on small and precise
actions, e.g., where to install a turret, where to hide from the enemy.
This dissertation work focuses on a novel path search method, that combines the state-of-theart
pathfinding algorithms with influence maps in order to achieve better time performance and
less memory space performance as well as more smooth paths in pathfinding.Algoritmos de pathfinding são usados por agentes inteligentes para resolver o problema do caminho
mais curto, desde a àrea jogos de computador até à robótica. Pathfinding é um tipo
particular de algoritmos de pesquisa, em que o objectivo é encontrar o caminho mais curto
entre dois nós. Um nó é um ponto no espaço onde um agente inteligente consegue navegar.
Agentes móveis em mundos físicos e virtuais são uma componente chave para a simulação de
comportamento inteligente. Se um agente não for capaz de navegar no ambiente que o rodeia
sem colidir com obstáculos, não aparenta ser inteligente. Consequentemente, pathfinding faz
parte das tarefas fundamentais de inteligencia artificial em vídeo jogos.
Algoritmos de pathfinding funcionam bem com agentes únicos a navegar por um ambiente. Em
jogos de estratégia em tempo real (RTS), potential fields (PF) são utilizados para a navegação
multi-agente em ambientes amplos e dinâmicos. Pelo contrário, os influence maps não são usados
no pathfinding. Influence maps são uma técnica de raciocínio espacial que ajudam agentes
inteligentes e jogadores a tomar decisões sobre o decorrer do jogo. Influence maps representam
informação de jogo, por exemplo, eventos e distribuição de poder, que são usados para
fornecer conhecimento aos agentes na tomada de decisões estratégicas ou táticas. As decisões
estratégicas são baseadas em atingir uma meta global, por exemplo, a captura de uma zona
do inimigo e ganhar o jogo. Decisões táticas são baseadas em acções pequenas e precisas, por
exemplo, em que local instalar uma torre de defesa, ou onde se esconder do inimigo.
Esta dissertação foca-se numa nova técnica que consiste em combinar algoritmos de pathfinding
com influence maps, afim de alcançar melhores performances a nível de tempo de pesquisa e
consumo de memória, assim como obter caminhos visualmente mais suaves
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