  Crowd counting has recently attracted increasing interest in computer vision
but remains a challenging problem. In this paper, we propose a trellis
encoder-decoder network (TEDnet) for crowd counting, which focuses on
generating high-quality density estimation maps. The major contributions are
four-fold. First, we develop a new trellis architecture that incorporates
multiple decoding paths to hierarchically aggregate features at different
encoding stages, which can handle large variations of objects. Second, we
design dense skip connections interleaved across paths to facilitate sufficient
multi-scale feature fusions and to absorb the supervision information. Third,
we propose a new combinatorial loss to enforce local coherence and spatial
correlation in density maps. By distributedly imposing this combinatorial loss
on intermediate outputs, gradient vanishing can be largely alleviated for
better back-propagation and faster convergence. Finally, our TEDnet achieves
new state-of-the art performance on four benchmarks, with an improvement up to
14% in terms of MAE.
