# Dynamic and Static Object Detection Considering Fusion Regions and Point-wise Features

Source: [Dynamic and Static Object Detection Considering Fusion Regions and Point-wise Features](<https://www.sota2.com/research/paper/68fa6a781e209131cea285d6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:25:36.663Z

Authors: Andr\\'es G\\'omez, Thomas Genevois, Jerome Lussereau, Christian Laugier

Published: 2021-07-27T09:42:18.000Z

## Abstract

Object detection is a critical problem for the safe interaction between autonomous vehicles and road users. Deep-learning methodologies allowed the development of object detection approaches with better performance. However, there is still the challenge to obtain more characteristics from the objects detected in real-time. The main reason is that more information from the environment's objects can improve the autonomous vehicle capacity to face different urban situations. This paper proposes a new approach to detect static and dynamic objects in front of an autonomous vehicle. Our approach can also get other characteristics from the objects detected, like their position, velocity, and heading. We develop our proposal fusing results of the environment's interpretations achieved of YoloV3 and a Bayesian filter. To demonstrate our proposal's performance, we asses it through a benchmark dataset and real-world data obtained from an autonomous platform. We compared the results achieved with another approach.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2107.12692>)

[PDF](<https://arxiv.org/pdf/2107.12692.pdf>)