Automotive Camera [Computer vision, Deep learning] - 2A

Perception of the Environment is a crucial step in the development of ADAS (Advanced Driver Assistance Systems) and Autonomous Driving. The main sensors that are widely accepted and used include Radar, Camera, LiDAR, and Ultrasonic. This course focuses on Cameras.

Specifically, with the advancement of deep learning and computer vision, the algorithm development approach in the field of cameras has drastically changed in the last few years. Many new students and people from other fields want to learn about this technology as it provides a great scope of development and job market. Many courses are also available to teach some topics of this development, but they are in parts and pieces, intended to teach only the individual concept.

In such a situation, even if someone understands how a specific concept works, the person finds it difficult to properly put in the form of a software module and also to be able to develop complete software from start to end which is demanded in most of the companies. This series which contains 3 courses - is designed systematically, so that by the end of the series, you will be ready to develop any perception-based complete end-to-end software application without hesitation and with confidence. Course 1 (already published and available online) - focuses on theoretical foundations Course 2A (This course) - focuses on the step-by-step implementation of camera processing module and object detector modules using Python 3.

x and object-oriented programming. course 2B (to be published very soon) - focuses on the step-by-step implementation of camera-based multi-object tracking (including Track object data structures, Kalman filters, tracker, data association, etc. ) using Python 3.