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Efficient Proposals: Scale Estimation for Object Proposals in Pedestrian Detection Tasks

Due to projective transformation, a great variety of pedestrian sizes appears on the image depending on their depths in the real world. In this letter, we analyze the object proposal generation strategies in existing object detection methods that underperform in some real-world applications owing to ignorance of spatial factors. To this end, we propose a neural network predictor to estimate the sizes of object proposals based on their locations on the image. Furthermore, our model can efficiently generate high-quality proposals using very few training samples with the help of a data augmentation strategy. The proposed size estimation method is compared against several state-of-art object proposal estimation methods by two metrics on a driving dataset and a train station surveillance dataset which shows significant performance advantages.