Lukas-Kanade-Template-Tracker

• The Lucas Kanade Template tracker has been implemented using python and OpenCV.
• The repository consists of the code and output videos.
• The outline of the pipeline followed is shown below. Further information can be found in the reference paper in the repository.

Outline of the Pipeline Followed :

A brief overview of the steps followed is given below. Technical background and the implementation of the LK tracking algorithm are described in Section 2 of Simon and Matthew's paper. The reference is provided below.

Reference : Lucas-Kanade 20 Years On: A Unifying Framework: Part 1 , Simon Baker and Iain Matthews, CMU-RI-TR-02-16

• A schematic overview of the Lucas-Kanade algorithm is shown above. The image I is warped with the current estimate of the warp in Step 1 and the result subtracted from the template in Step 2 to yield the error image. The gradient of I is warped in Step 3, the Jacobian is computed in Step 4, and the two combined in Step 5 to give the steepest descent images. In Step 6 the Hessian is computed from the steepest descent images. In Step 7 the steepest descent parameter updates are computed by dot producting the error image with the steepest descent images. In Step 8 the Hessian is inverted and multiplied by the steepest descent parameter updates to get the final parameter updates delta p which are then added to the parameters p in Step 9.

Output (Output videos can be found in the repository)

Output for Car Tracking

Output for Human Tracking