LK-based Optical Flow Field Computation and Tracking

In this text: I have independently developed a Lucas-Kanade (LK) optical flow field computation and tracking algorithm. The algorithm utilizes the LK optical flow method to calculate motion information within images and track target positions. This implementation computes pixel displacement vectors between consecutive frames using gradient descent optimization, where the key functions involve calculating spatial and temporal gradients through convolution operations with Sobel or similar kernels. Through this method, the algorithm can capture object motion trajectories and velocity information in image sequences. This implementation represents an enhanced version of the basic LK optical flow method, incorporating pyramid-based multi-scale processing to improve computation accuracy and tracking efficiency. The code structure includes pyramid construction, warp estimation, and iterative refinement layers to handle larger displacements while maintaining computational stability. By employing this algorithm, researchers can better analyze motion patterns in video sequences, providing foundational capabilities for subsequent computer vision applications such as motion analysis, object tracking, and video stabilization. The implementation features configurable parameters for pyramid levels, window sizes, and termination criteria to balance between precision and performance requirements.