BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving

TL;DR

This paper introduces BEV-MODNet, an end-to-end monocular camera-based system for detecting moving objects directly in Bird's Eye View space, along with a new dataset for training and evaluation.

Contribution

The work presents a novel dataset and a two-stream neural network architecture for direct BEV motion segmentation from monocular images, improving over traditional inverse perspective mapping methods.

Findings

Achieved a 13% improvement in mIoU over baseline methods.

Created a new dataset with 12.9k images and BEV annotations.

Demonstrated effective direct BEV motion segmentation from monocular input.

Abstract

Detection of moving objects is a very important task in autonomous driving systems. After the perception phase, motion planning is typically performed in Bird's Eye View (BEV) space. This would require projection of objects detected on the image plane to top view BEV plane. Such a projection is prone to errors due to lack of depth information and noisy mapping in far away areas. CNNs can leverage the global context in the scene to project better. In this work, we explore end-to-end Moving Object Detection (MOD) on the BEV map directly using monocular images as input. To the best of our knowledge, such a dataset does not exist and we create an extended KITTI-raw dataset consisting of 12.9k images with annotations of moving object masks in BEV space for five classes. The dataset is intended to be used for class agnostic motion cue based object detection and classes are provided as meta-data for better tuning. We design and implement a two-stream RGB and optical flow fusion architecture which outputs motion segmentation directly in BEV space. We compare it with inverse perspective mapping of state-of-the-art motion segmentation predictions on the image plane. We observe a significant improvement of 13% in mIoU using the simple baseline implementation. This demonstrates the ability to directly learn motion segmentation output in BEV space. Qualitative results of our baseline and the dataset annotations can be found in https://sites.google.com/view/bev-modnet.