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

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