Pose Based Computer Vision Controls For Generic Game Interaction.pptx

Developed an innovative game controller using computer vision techniques that leverages pose detection to
interact with the popular game "Subway Surfers." The project uses Mediapipe for real-time pose
estimation, enabling hands-free control of the game character through body movements.

1. Welcome to Pose-Based Gaming Discover a new frontier in gaming where your body movements and poses become the controller. Immerse yourself in dynamic, interactive experiences powered by advanced computer vision technology.

2. Introduction to Pose-Based Gaming Explore the emerging world of gaming controlled by human body movements and poses.

3. Abstract This project presents a novel approach to game control using computer vision techniques for pose detection. By leveraging machine learning algorithms, specifically pose estimation models, the system interprets the user's body movements to generate control commands for games. The proposed system aims to provide intuitive and immersive gaming experiences by mapping common game actions such as 'up', 'down', 'left', and 'right' to corresponding detected poses. Unlike traditional input devices, this method offers greater freedom of movement, potentially enhancing accessibility for players with disabilities or limited mobility. The versatility of the system allows integration with various game genres, providing a seamless and adaptable control mechanism across different gaming platforms. Experimental results demonstrate the feasibility and effectiveness of the proposed approach, showcasing its potential to revolutionize game interaction through natural body gestures.

4. Existing System Current gaming systems rely heavily on traditional input devices such as controllers, keyboards, and mice. These input methods can be limiting, as they require users to master complex button combinations and movements. This can present challenges for players with disabilities or those seeking more natural and intuitive control schemes. Conventional input devices often require a high degree of fine motor skills and coordination Limited accessibility for users with physical limitations or mobility impairments Lack of seamless integration with the player's natural body movements and poses

5. Proposed System 1 Natural Pose Detection The proposed system leverages advanced computer vision techniques to detect the user's body poses in real-time, enabling intuitive game control through natural movements. 2 Seamless Integration The system seamlessly integrates with various game platforms, allowing for a seamless and adaptable control mechanism across different gaming experiences. 3 Enhanced Accessibility By using body poses as the input method, the system aims to enhance accessibility for players with disabilities or limited mobility, providing a more inclusive gaming experience.

6. UML Diagrams Class Diagram

7. RESULT

8. Pose Detection Pose detection is the process of identifying and tracking the key joints and limbs of the human body within digital images or video frames. Advanced computer vision algorithms leverage machine learning to accurately detect and classify human poses, enabling new applications in areas like gaming, fitness, and assistive technology.

9. Conclusion and Key Takeaways Immersive Experiences Pose-based gaming enables more natural and immersive interactions. Expanded Accessibility Opens up gaming to a wider range of users. Fitness and Health Benefits Encourages physical activity and healthy gameplay. Emerging Trends Multimodal interactions and AR experiences are on the horizon.

10. Thank you