Object Detection is USELESS

One of the biggest misunderstanding among students and outsider robotic learners is that Object Detection = Self-Driving Cars.

​Somehow, this misconception keeps surviving, and many spend weeks learning all the details about the latest version of YOLO. In reality, self-driving cars are NOT using object detectors like YOLO as their main perception pipeline.

It can be a tool, that is later fused with point clouds, or used for region filtering, or other... but rarely the end algorithm.

​Instead, companies focus on Object Tracking. Knowing that an object is at pixel (300,300) is USELESS to a self-driving car. But knowing that the car is crossing our lane is useful.

​Object Tracking turns detection into a useful feature.

Object Tracking in Action

The best way to learn it? Meet the...

Perception/Tracking Courses

OBSTACLE TRACKING

To any Perception engineer, whether computer vision or LiDAR, I would recommend first and foremost to learn multi-object tracking.

KALMAN FILTERS

Kalman Filters are the swiss knife of engineers. They're inside rockets, drones, state estimation systems, onboard planes, phones, and of course, self-driving cars. This course will teach these to you in a few hours.

4D PERCEPTION

4D Perception is the most advanced course from the entire catalogue. It combines skills from 2D tracking, Kalman Filters, and 3D Perception. Do this course only when you feel prepared.

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The Self-Driving Car App

OVER 5 HOURS OF SELF-DRIVING CAR CONTENT

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