Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network

Abstract

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.

CVPR 2019, Accepted

References in corpus (8)

• Adam: A Method for Stochastic Optimization
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• AutoScale: Learning to Scale for Crowd Counting and Localization
• Towards Using Count-level Weak Supervision for Crowd Counting
• Plug-and-Play Rescaling Based Crowd Counting in Static Images
• NAS-Count: Counting-by-Density with Neural Architecture Search
• Bayesian Multi-Scale Neural Network for Crowd Counting
• Estimating People Flows to Better Count Them in Crowded Scenes
• 3D Crowd Counting via Geometric Attention-guided Multi-View Fusion
• Weighing Counts: Sequential Crowd Counting by Reinforcement Learning
• Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs
• Multi-Level Attentive Convoluntional Neural Network for Crowd Counting
• Efficient Crowd Counting via Structured Knowledge Transfer
• Multi-channel Deep Supervision for Crowd Counting
• Active Crowd Counting with Limited Supervision
• Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting