Calculating Vehicle Speed and Integrating GPS Data for Real-Time Tracking

June 2, 2024
Combining Detection, Tracking, and GPS for Accurate Vehicle Speed Measurement
Learn how to calculate vehicle speed and integrate GPS data for real-time tracking. This guide combines detection, tracking, and accurate speed measurement techniques for enhanced driving safety.
Gunn Kim
Page ID
Introduction In this final part, we will integrate all components to measure the speed of vehicles in front of you. We will also use GPS data to enhance accuracy.

1. Calculating Speed To calculate the speed, you need to measure the distance traveled by the vehicle over time.
import time previous_time = time.time() previous_distance = None def calculate_distance(bbox): # Custom function to calculate distance based on bbox size # This is a placeholder for the actual distance calculation logic return bbox[2] - bbox[0] def calculate_speed(distance_initial, distance_final, time_elapsed): distance_change = distance_final - distance_initial speed_m_s = distance_change / time_elapsed speed_kmh = speed_m_s * 3.6 return speed_kmh
2. Integrating GPS Data Use the smartphone’s GPS to get your vehicle’s speed. This can be done through various Android APIs or libraries.
# Placeholder for getting the vehicle's speed from GPS def get_my_vehicle_speed(): return 50 # Assume a constant speed for demonstration
3. Combining Everything Combine all the components to calculate the relative speed of the vehicle in front.
while cap.isOpened(): ret, frame = if not ret: break results = model.predict(source=frame) bboxes = extract_bboxes(results) outputs = deepsort.update(bboxes) current_time = time.time() for output in outputs: bbox = output[:4] track_id = output[4] current_distance = calculate_distance(bbox) if previous_distance is not None: time_elapsed = current_time - previous_time relative_speed = calculate_speed(previous_distance, current_distance, time_elapsed) my_vehicle_speed = get_my_vehicle_speed() front_vehicle_speed = my_vehicle_speed + relative_speed print(f'Front Vehicle Speed: {front_vehicle_speed} km/h') previous_time = current_time previous_distance = current_distance
4. Enhancing Accuracy
  • Calibration: Calibrate your system to improve accuracy.
  • Frame Rate: Ensure the video processing frame rate is high to minimize latency.
Conclusion By following this series, you have learned how to build a system to measure the speed of vehicles in front of you using a smartphone. This involves capturing video, detecting and tracking vehicles, and calculating their speed using real-time data.

  1. How do I calculate the speed of a vehicle? Speed is calculated by measuring the distance traveled over a period and then dividing this distance by the time taken.
  1. How can I integrate GPS data into my system? You can use GPS data to get the speed of your vehicle and then calculate the relative speed of the vehicle in front by comparing positional changes over time.
  1. What are the challenges in real-time speed calculation? The main challenges include ensuring low latency, accurate distance measurement, and handling varying frame rates and environmental conditions.
  1. How do I enhance the accuracy of my system? Calibrating your system, ensuring high frame rates, and using precise distance measurement methods can significantly enhance accuracy.

By following this structure, each part of the series will provide a comprehensive overview, practical implementation steps, and additional resources for further learning. This approach ensures that readers of all levels, including high school students, can understand and apply the concepts.