develop code to track an object

I need to develop a code able to determine , track, and measure distance for an object. the pdf attached file will gave you a better explanation.

[Image Analyst]

OK. Good luck.

If you'd rather use already-developed code, the Computer Vision System Toolbox has lots of tracking capabilities.

Answer 1

[Abhishek Singh]

Hi Sultan,

You may look into the Computer Vision System Toolbox which provides algorithms, functions, and apps for designing and simulating computer vision and video processing systems. You can perform feature detection, extraction, and matching, as well as object detection and tracking. For 3-D computer vision, the system toolbox supports single, stereo, and fisheye camera calibration; stereo vision; 3-D reconstruction; and 3-D point cloud processing.

Algorithms for deep learning and machine learning enable you to detect faces, pedestrians, and other common objects using pretrained detectors. You can train a custom detector using ground truth labeling with training frameworks such as Faster R-CNN and ACF. You can also classify image categories and perform semantic segmentation.

Algorithms are available as MATLAB functions, System objects, and Simulink blocks.

You may find these links to be useful:

Feature Matching - https://www.mathworks.com/help/releases/R2018a/vision/examples/find-image-rotation-and-scale-using-automated-feature-matching.html

Image registration - https://www.mathworks.com/help/releases/R2018a/vision/examples/feature-based-panoramic-image-stitching.html

Camera Calibration - https://www.mathworks.com/help/releases/R2018a/vision/ug/camera-calibration.html

Object Tracking - https://www.mathworks.com/help/releases/R2018a/vision/examples/face-detection-and-tracking-using-the-klt-algorithm.html

Image Labeling - https://www.mathworks.com/help/releases/R2018a/vision/ug/define-ground-truth-for-image-collections.html

Object detection using deep learning - https://www.mathworks.com/help/releases/R2018a/vision/examples/object-detection-using-deep-learning.html