  Crowd counting is a task worth exploring in modern society because of its
wide applications such as public safety and video monitoring. Many CNN-based
approaches have been proposed to improve the accuracy of estimation, but there
are some inherent issues affect the performance, such as overfitting and
details lost caused by pooling layers. To tackle these problems, in this paper,
we propose an effective network called MDSNet, which introduces a novel
supervision framework called Multi-channel Deep Supervision (MDS). The MDS
conducts channel-wise supervision on the decoder of the estimation model to
help generate the density maps. To obtain the accurate supervision information
of different channels, the MDSNet employs an auxiliary network called
SupervisionNet (SN) to generate abundant supervision maps based on existing
groundtruth. Besides the traditional density map supervision, we also use the
SN to convert the dot annotations into continuous supervision information and
conduct dot supervision in the MDSNet. Extensive experiments on several
mainstream benchmarks show that the proposed MDSNet achieves competitive
results and the MDS significantly improves the performance without changing the
network structure.
