RC Lap Timing Software -- 2

Single-Camera / RFID Lap Timing System for RC Racing Arena
Project Overview
We are building a standalone lap-timing system for an indoor RC car racing arena, using a single fixed-position input at the finish line.
The system must detect each car crossing the finish line and calculate accurate lap times in real time, displaying results live on a dedicated monitor beside the track.
Detection may be implemented using:
RFID reader
Computer vision (OpenCV / ArUco / object tracking)
Hybrid approach (preferred if it improves reliability)
The priority is accuracy, stability, and simplicity, not experimental or over-engineered solutions.

Project Objectives

Real-time lap detection with ±0.05 second accuracy
Clear on-screen display suitable for public viewing
Simple, reproducible setup on a Windows PC
Clean, maintainable codebase suitable for future expansion

Core Functional Requirements
1. Lap Detection & Timing
Detect each car crossing the finish line using:
RFID read and/or Camera-based trigger (line-crossing, marker detection, or similar)
Timestamp crossings instantly and compute lap time
Handle rapid consecutive crossings without missed reads


2. Real-Time Display
Live lap times shown on a dedicated monitor
Display options: Full-screen or windowed mode
Immediate refresh on every lap detection

3. Timing Analytics
The system must continuously compute and display:
Fastest lap (best lap) per session
Average lap time per driver
Current lap count

4. Driver / Car Identification
Each car must be uniquely identified using one method chosen by the developer, such as:
RFID tags
Visual markers (ArUco, number plate, color tag)
IR beacon or equivalent
All lap times must be correctly assigned to the corresponding car without manual intervention during the race.

5. Lap History & Export
Maintain a scrollable lap history log
Export session data (CSV or similar) after the race
Log should include:
Car ID
Lap number
Lap time
Timestamp

Platform & Technical Constraints
Operating System: NEED TO DECIDE ( OPEN FOR OPTIONS)
Inputs: Standard USB camera and/or USB RFID reader
Display: HDMI/monitor output

Languages/Frameworks: NEED TO DECIDE ( OPEN FOR OPTIONS)
(Setup must be simple and well-documented) Python, C++, OpenCV, TensorFlow, or equivalent
MANDATORY: Must run offline (no internet dependency)

Deliverables
Hardware specification
List of electrical components required
Preference for plug-and-play USB devices
Mounting recommendations if applicable
Working application
Compiled executable or Clean Python environment with requirements file
Source code
Fully documented
Logical structure
No hard-coded environment dependencies
Setup & Configuration Guide
Camera/RFID placement
Calibration steps
Screen layout customization
Common failure modes and fixes
Accuracy Proof
Short test video or RFID test run
Matching timing log
Demonstrated accuracy within ±0.05 seconds

Milestones (Suggested)
Milestone 1 – System Design (20%)
Architecture overview
Detection method finalized
Hardware list approved
Milestone 2 – Core Detection Engine (30%)
Reliable lap detection
Timing accuracy validated
Milestone 3 – UI & Analytics (25%)
Live display
Best/average lap calculations
Lap history logging
Milestone 4 – Final Delivery & Validation (25%)
Clean install
Documentation
Accuracy test submission

Future Scope (Not in Current Budget)
Multi-camera split timing
Cloud leaderboard
Player login and replay integration
Design decisions should not block these future extensions.

Ideal Freelancer Profile
Experience with:
Computer vision or RFID systems
Sports timing, object tracking, or OCR-like problems
Strong debugging and real-world hardware integration skills
Able to prioritize robustness over novelty

Proposal Requirements
Please include:
Relevant past work (vision, RFID, or timing systems)
Proposed detection approach (camera, RFID, or hybrid)
Estimated timeline
Total cost with milestone breakdown
Assumptions or constraints you foresee

Evaluation Criteria
Technical soundness of approach
Proven experience with similar systems
Clarity of documentation and communication
Ability to meet accuracy and reliability targets