Scene Generalized Multi-View Pedestrian Detection with Rotation-Based Augmentation and Regularization

Abstract

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Multi-view pedestrian detection aims to predict a bird’s eye view (BEV) occupancy map using multiple camera views. While existing deep learning-based methods have shown progress, they are typically trained and tested on the same scene, which has the same camera layout and number of cameras. As a result, they are difficult to generalize to new scenes. A dataset containing multiple scenes has recently been proposed to overcome this limitation, but even with this dataset, the performance on new scenes remains poor due to overfitting to the limited training scenes. To address this problem, we propose a novel data augmentation and regularization method for multi-view pedestrian detection. The unique point of our method is that it rotates the BEV features to generate the features originating from a new scene. This enables our method to effectively train the detection model with the knowledge of new scenes and prevent overfitting to specific scenes through regularization. Experimental results show that our method outperforms existing methods in terms of generalization to new scenes.