This post explores road segmentation using a U-Net architecture trained on monocular images from the KITTI dataset. The task focuses on identifying drivable areas from RGB inputs, a challenge due to lighting, shadows, and variable road conditions. I detail the model architecture, training strategies, and key hyperparameters such as learning rate selection and loss functions.

This post explores camera-based object tracking through the implementation of the Simple Online and Realtime Tracker (SORT) algorithm. It covers key concepts and foundational elements essential for object tracking applications, demonstrating them with a SORT implementation for automotive tracking using the KITTI dataset. While SORT is computationally efficient and relatively simple, it delivers solid performance in the KITTI object tracking challenge.

A performance comparison between the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) in a simulated environment

A shallow CNN implementation to classify targets from the range-Doppler map of a FMCW radar