Amplifying the Anterior-Posterior Difference via Data Enhancement -- A More Robust Deep Monocular Orientation Estimation Solution

TL;DR

This paper introduces a pretraining approach that predicts the semicircle containing an object's orientation to improve monocular orientation estimation accuracy and reduce anterior-posterior confusion in traffic scene objects.

Contribution

The paper proposes a novel semicircle prediction pretraining method that enhances orientation estimation robustness without complex network modifications.

Findings

Improved orientation estimation accuracy with the proposed method.

Mitigated anterior-posterior confusion in object orientation predictions.

Achieved state-of-the-art performance with a simpler backbone architecture.

Abstract

Existing deep-learning based monocular orientation estimation algorithms faces the problem of confusion between the anterior and posterior parts of the objects, caused by the feature similarity of such parts in typical objects in traffic scenes such as cars and pedestrians. While difficult to solve, the problem may lead to serious orientation estimation errors, and pose threats to the upcoming decision making process of the ego vehicle, since the predicted tracks of objects may have directions opposite to ground truths. In this paper, we mitigate this problem by proposing a pretraining method. The method focuses on predicting the left/right semicircle in which the orientation of the object is located. The trained semicircle prediction model is then integrated into the orientation angle estimation model which predicts a value in range $[0, \pi]$. Experiment results show that the proposed semicircle prediction enhances the accuracy of orientation estimation, and mitigates the problem stated above. With the proposed method, a backbone achieves similar state-of-the-art orientation estimation performance to existing approaches with well-designed network structures.