Feature-Aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance

With the development of deep neural networks, the performance of crowd counting and pixel-wise density estimation is continually being refreshed. Despite this, there are still two challenging problems in this field: 1) current supervised learning needs a large amount of training data, but collecting and annotating them is difficult and 2) existing methods cannot generalize well to the unseen domain. A recently released synthetic crowd dataset alleviates these two problems. However, the domain gap between the real-world data and synthetic images decreases the models' performance. To reduce the gap, in this article, we propose a domain-adaptation-style crowd counting method, which can effectively adapt the model from synthetic data to the specific real-world scenes. It consists of multilevel feature-aware adaptation (MFA) and structured density map alignment (SDA). To be specific, MFA boosts the model to extract domain-invariant features from multiple layers. SDA guarantees the network outputs fine density maps with a reasonable distribution on the real domain. Finally, we evaluate the proposed method on four mainstream surveillance crowd datasets, Shanghai Tech Part B, WorldExpo'10, Mall, and UCSD. Extensive experiments are evidence that our approach outperforms the state-of-the-art methods for the same cross-domain counting problem.

Automated summary

With the advancement of deep neural networks, the performance of crowd counting and pixel-wise density estimation has been improving, but challenges persist. A synthetic crowd dataset released recently helps address the difficulty in collecting data, but the domain gap between real data and synthetic images hinders model performance. To tackle this, a domain-adaptation-style crowd counting method is proposed in this article, which effectively adapts the model from synthetic data to specific real-world scenes.