FCHD: A fast and accurate head detector

In this paper, we propose FCHD-Fully Convolutional Head Detector, which is an end-to-end trainable head detection model, which runs at 5 fps and with 0.70 average precision (AP), on a very modest GPU. Recent head detection techniques have avoided using anchors as a starting point for detection especially in the cases where the detection has to happen in the wild. The reason is poor performance of anchor-based techniques under scenarios where the object size is small. We argue that a good AP can be obtained with carefully designed anchors, where the anchor design choices are made based on the receptive field size of the hidden layers. Our contribution is two folds. 1) A simple fully convolutional anchor based model which is end-to-end trainable and has a very low inference time. 2) Carefully chosen anchor sizes which play a key role in getting good average precision. Our model achieves comparable results than many other baselines on challenging head detection dataset like BRAINWASH. Along with accuracy, our model has least runtime among all the baselines along with modest hardware requirements which makes it suitable for edge deployments in surveillance applications. The code is made open-source at https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector.