# Some Aspects of Geometric Computer Vision for Analysing Dynamical Scenes focusing Automotive Applications

## TL;DR

This paper reviews fundamental geometric computer vision principles for automotive applications, emphasizing robust, real-time algorithms for processing camera image streams on moving vehicles.

## Contribution

It introduces key geometric relations and shares practical insights for implementing robust, real-time vision algorithms like optical flow and visual odometry in automotive contexts.

## Key findings

- Highlights the importance of robustness in real-time vision algorithms
- Provides practical considerations for algorithm implementation
- Discusses interrelations and critical aspects of geometric estimates

## Abstract

This draft summarizes some basics about geometric computer vision needed to implement efficient computer vision algorithms for applications that use measurements from at least one digital camera mounted on a moving platform with a special focus on automotive applications processing image streams taken from cameras mounted on a car. Our intention is twofold: On the one hand, we would like to introduce well-known basic geometric relations in a compact way that can also be found in lecture books about geometric computer vision like [1, 2]. On the other hand, we would like to share some experience about subtleties that should be taken into account in order to set up quite simple but robust and fast vision algorithms that are able to run in real time. We added a conglomeration of literature, we found to be relevant when implementing basic algorithms like optical flow, visual odometry and structure from motion. The reader should get some feeling about how the estimates of these algorithms are interrelated, which parts of the algorithms are critical in terms of robustness and what kind of additional assumptions can be useful to constrain the solution space of the underlying usually non-convex optimization problems.