DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera
This provides a robust solution for people detection in applications like surveillance or crowd monitoring, though it appears incremental as it builds on existing encoder-decoder and residual layer architectures.
The paper tackles the problem of detecting multiple people from a single overhead depth image by proposing DPDnet, a deep learning method that achieves over 99% accuracy on three public datasets and runs in real-time on conventional GPUs.
In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability. Our neural network, called DPDnet, is based on two fully-convolutional encoder-decoder neural blocks based on residual layers. The Main Block takes a depth image as input and generates a pixel-wise confidence map, where each detected person in the image is represented by a Gaussian-like distribution. The refinement block combines the depth image and the output from the main block, to refine the confidence map. Both blocks are simultaneously trained end-to-end using depth images and head position labels. The experimental work shows that DPDNet outperforms state-of-the-art methods, with accuracies greater than 99% in three different publicly available datasets, without retraining not fine-tuning. In addition, the computational complexity of our proposal is independent of the number of people in the scene and runs in real time using conventional GPUs.