Is it possible to estimate the real-world distance of a detected object from the camera ?

Estimating the real-world distance of a detected object from a camera using only a single 2D image is challenging but possible with certain limitations and assumptions.

Using only image data and detection results (like bounding boxes), you can implement these approaches:

1. Known object size method: If you know the actual physical dimensions of the detected object (like standard vehicle sizes), you can estimate distance using the relationship between the apparent size in the image and the actual size. This requires camera calibration information (focal length).

2. Perspective geometry: Using the position of objects relative to the horizon line or vanishing points in the image, along with camera height and angle information.

3. Machine learning approaches: Training models to estimate distance based on visual cues in the image, though this requires training data with ground truth distances.

However, these methods have limitations in accuracy. For more precise distance measurements, you would need:

• Depth sensors (LiDAR, ToF cameras)
• Stereo vision (two cameras)
• Structured light systems
• Sequential frames if the camera or object is moving (structure from motion)

The best approach depends on your accuracy requirements and hardware constraints. If you're working with a single camera and need approximate distances, the known object size method is often most practical, but requires calibration of your specific camera setup and knowledge of the objects you're detecting.
Sources
PositionalAccuracy - Amazon Location Service