论文标题
通过数据增强扩增前后差 - 更坚固的深层单眼估计解决方案
Amplifying the Anterior-Posterior Difference via Data Enhancement -- A More Robust Deep Monocular Orientation Estimation Solution
论文作者
论文摘要
现有的基于深度学习的单眼取向估计算法面临着物体前部和后部之间的混淆问题，这是由于这些部分在汽车和行人等交通场景中的特征相似性引起的。尽管难以解决，但问题可能会导致严重的定向估计错误，并对即将到来的自我车辆的决策过程构成威胁，因为对象的预测轨道可能与地面真相相反。在本文中，我们通过提出预处理方法来减轻此问题。该方法着重于预测对象方向所在的左/右半圆。然后将训练的半圆预测模型集成到方向角度估计模型中，该模型预测范围$ [0，π] $中的值。实验结果表明，所提出的半圆预测提高了方向估计的准确性，并减轻了上述问题。通过提出的方法，一个骨干可以与具有精心设计的网络结构的现有方法达到类似的最新方向估计性能。
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 $[0, π]$. 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.
