Improved Object Proposals with Geometrical Features for Autonomous Driving

Abstract EN

This paper aims at generating high-quality object proposals for object detection in autonomous driving.

Most existing proposal generation methods are designed for the general object detection, which may not perform well in a particular scene.

We propose several geometrical features suited for autonomous driving and integrate them into state-of-the-art general proposal generation methods.

In particular, we formulate the integration as a feature fusion problem by fusing the geometrical features with existing proposal generation methods in a Bayesian framework.

Experiments on the challenging KITTI benchmark demonstrate that our approach improves the existing methods significantly.

Combined with a convolutional neural net detector, our approach achieves state-of-the-art performance on all three KITTI object classes.