This paper presents a modified version of the Lucas-Kanade (LK) method that incorporates depth maps to enhance the accuracy of tracking. Traditional LK methods, widely adopted for optical flow estimation, typically rely on intensity values of pixels to estimate motion. However, such approaches are often vulnerable to challenges presented by texture-less regions, perspective shifts, and occlusions. By integrating depth maps into the LK framework, this method adds a valuable dimension of spatial information that alleviates these limitations. This approach utilizes depth values to filter out layers of the scene not relevant to the point of interest, providing a more robust descriptor for tracking applications. Experimental results on custom datasets demonstrate that the depth aware LK method generally outperforms conventional LK algorithms in terms of accuracy and robustness. Furthermore, potential applications and the broader implications of said application are discussed.