Object Counting with Small Datasets of Large Images
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We explore the problem of training one-look regression models for counting objects in datasets comprising a small number of high-resolution, variable-shaped images. To reduce overfitting when training on full resolution samples, we propose to use global sum pooling (GSP) instead of global average pooling (GAP) or fully connected (FC) layers at the backend of a convolutional neural network. Although computationally equivalent to GAP, we show via detailed experimentation that GSP allows convolutional networks to learn the counting task as a simple linear mapping problem generalized over the input shape and the number of objects present. We evaluate our approach on four different aerial image datasets - three car counting datasets (CARPK, PUCPR+, and COWC) and one new challenging dataset for wheat spike counting. Our GSP approach achieves state-of-the-art performance on all four datasets and GSP models trained with smaller-sized image patches localize objects better than their GAP counterparts.
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