3D Object Recognition and Tracking Project

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In recent years, 3D object recognition and tracking have gained significant traction in various fields, from augmented reality applications to robotics. One of the key technologies driving this advancement is MediaPipe, a versatile library that provides robust solutions for computer vision tasks.

In this 3D Object Detection Project, we will explore the fundamentals of 3D object recognition and tracking, compare it with its 2D counterpart, and delve into the practical implementation using the MediaPipe library.

3D object recognition and tracking involve identifying and following the movement of three-dimensional objects within a given environment. This technology has broad applications, such as in robotics, where it enables machines to perceive and interact with the three-dimensional world around them.

Unlike traditional 2D tracking, which operates in a two-dimensional space, 3D tracking considers depth information, allowing for a more accurate representation of the real-world scenario.

Use of 3D Object Tracking

Robotics: 3D object tracking is integral to robotic systems, enabling them to navigate and interact with their surroundings more effectively.

Augmented Reality: In AR applications, 3D tracking enhances realism by accurately placing virtual objects in the physical environment.

Virtual Reality: VR experiences benefit from 3D tracking, ensuring a more immersive and interactive user experience.

Also Check: Augmented Reality vs. Virtual Reality

3D vs. 2D Object Tracking and Recognition

The main difference between 3D and 2D tracking lies in the dimensionality of the data being processed. While 2D tracking is limited to the x and y axes, 3D object tracking adds the z-axis, representing depth.

This additional dimension allows for a more comprehensive understanding of the spatial relationships between objects, providing enhanced accuracy in tracking and recognition tasks. 3D object detection is particularly beneficial when dealing with scenarios where depth information is crucial, such as in robotics, virtual reality, and augmented reality applications.

3D Object Recognition and Tracking
Fig 1. 2D vs 3D object tracking

Bill of Materials

To implement 3D object tracking using MediaPipe, you will need the following:

Components Description Quantity  Price
Raspberry Pi 4/Apple M! Chip SBC 1 4000
USB Camera 1 500


First, ensure you have Python installed on your device. Then, install the MediaPipe and OpenCV library using the following command:

sudo pip3 install mediapipe

sudo pip3 install python-opencv

First, import the OpenCV and Medipipe modules in the code. Then create a function to access the camera and grab the video using an OpenCV, process the video into the frames.

Then transfer that image to the Mediapipe for image processing and recognition and detection of the object in each frame. Then we use the OpenCV to draw the 3D lines wrapping the object in 3D. 

3D Object Tracking Code
Fig 2. Code for 3D object tracking

In the code also set the model name and object name to track and detect in the 3D.

Code for 3D Object Recognition
Fig 3. Code for configuration of Mediapipe for detecting the object in 3D

Testing 3D Object Recognition and Tracking

3D Object Detection and Tracking Project
Fig 4. Testing and tracking the object in 3D.

Now execute the code, and it will display the video frames while detecting the specified object. Additionally, it will generate 3D wrapping lines around the object to track it in real time in a 3D space.

If you’re running the code on a Raspberry Pi 4, you may encounter a lower frames-per-second (FPS) rate and slower processing. However, when running the same code on an Apple M1 chip, the performance is significantly improved, achieving fast, real-time tracking with accurate 3D object positioning.

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