  In this paper, we propose FCHD-Fully Convolutional Head Detector, an
end-to-end trainable head detection model. Our proposed architecture is a
single fully convolutional network which is responsible for both bounding box
prediction and classification. This makes our model lightweight with low
inference time and memory requirements. Along with run-time, our model has
better overall average precision (AP) which is achieved by selection of anchor
sizes based on the effective receptive field of the network. This can be
concluded from our experiments on several head detection datasets with varying
head counts. We achieve an AP of 0.70 on a challenging head detection dataset
which is comparable to some standard benchmarks. Along with this our model runs
at 5 FPS on Nvidia Quadro M1000M for VGA resolution images. Code is available
at https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector.
