Question About Final Project Complexity

Issue Overview

Users are seeking guidance on the expected complexity for a final project in an AI fundamentals course, specifically regarding the implementation of a project centered around basketball. The user is considering whether robust tracking of a basketball would suffice or if they should aim for more complex features like shot detection or dribbling move recognition. The inquiry reflects uncertainty about project expectations and the level of sophistication required for successful completion.

Relevant Hardware and Software Specifications

• Project Focus: Basketball tracking and analysis
• Potential Features:
  • Robust tracking of the basketball
  • Shot detection
  • Dribbling move recognition

Symptoms

• Uncertainty regarding project complexity and expectations.
• Lack of clarity on whether basic tracking is sufficient or if additional features are necessary.

Possible Causes

1. Ambiguity in Project Guidelines: The rubric or guidelines for the final project may not clearly define the expected complexity, leading to confusion among students.
2. Varied Interpretations of Complexity: Different users may have different interpretations of what constitutes a "complex" project, leading to questions about scope and depth.
3. Limited Experience with AI Projects: As beginners, users may not have enough experience to gauge what level of complexity is achievable within the given timeframe.

Troubleshooting Steps, Solutions & Fixes

Step-by-Step Instructions

1. Review Project Rubric:

   • Carefully read through the provided rubric to identify any specific criteria related to project complexity.

2. Consult Course Materials:

   • Look for examples of past projects or additional resources provided during the course that may illustrate expected complexity levels.

3. Engage with Instructors or Peers:

   • Reach out to course instructors or classmates for clarification on expectations regarding project complexity.

4. Research Existing Projects:

   • Investigate existing projects related to basketball tracking to understand common features and complexities. For example, explore:
     • GitHub repositories focusing on basketball action tracking.
     • Tutorials on object detection and tracking using AI frameworks such as YOLO or OpenCV.

5. Consider Incremental Development:

   • Start with basic tracking functionality and gradually add more complex features (like shot detection) as time permits.

6. Utilize Available Resources:

   • Leverage online tutorials, such as those provided by NVIDIA’s Jetson Inference repository, which includes DNN-based object tracking:

     wget https://nvidia.box.com/shared/static/veuuimq6pwvd62p9fresqhrrmfqz0e2f.mp4 -O pedestrians.mp4
     

7. Document Your Progress:

   • Keep track of your development process and any challenges faced, which can help in discussions with instructors about project expectations.

Recommended Fixes

• Users are encouraged to start with robust tracking as a core feature while remaining open to adding more complex functionalities based on time and resources available.
• Engaging with community resources and existing codebases can provide valuable insights into achievable project goals.

Best Practices for Future Prevention

• Ensure clarity in project guidelines by requesting detailed rubrics or examples from instructors at the beginning of the course.
• Regularly check in with peers or mentors during the development process to align on expectations and progress.

Unresolved Aspects and Further Investigation

• Users continue to seek specific examples of projects that meet the expected complexity criteria for better guidance.
• There may be additional resources or tutorials that could help clarify the scope of AI projects in sports analytics that are not currently identified.

By following these troubleshooting steps and solutions, users can effectively navigate their final project requirements while developing a basketball-related AI application that meets their course expectations.