Scene-Adaptive Attention Network for Crowd Counting
In recent years, significant progress has been made on the research of crowd counting. However, as the challenging scale variations and complex scenes existed in crowds, neither traditional convolution networks nor recent Transformer architectures with fixed-size attention could handle the task well. To address this problem, this paper proposes a scene-adaptive attention network, termed SAANet. First of all, we design a deformable attention in-built Transformer backbone, which learns adaptive feature representations with deformable sampling locations and dynamic attention weights. Then we propose the multi-level feature fusion and count-attentive feature enhancement modules further, to strengthen feature representation under the global image context. The learned representations could attend to the foreground and are adaptive to different scales of crowds. We conduct extensive experiments on four challenging crowd counting benchmarks, demonstrating that our method achieves state-of-the-art performance. Especially, our method currently ranks No.1 on the public leaderboard of the NWPU-Crowd benchmark. We hope our method could be a strong baseline to support future research in crowd counting. The source code will be released to the community.
Code (0)
등록된 구현이 없습니다.
Tasks
Crowd CountingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
In real-world crowd counting applications, the crowd densities vary greatly in spatial and temporal domains. A detection based counting method will estimate crowds accurately in low density scenes, while its reliability …
Crowd CountingDensity EstimationregressionTransformer-Based Dual-Optical Attention Fusion Crowd Head Point Counting and Localization Network
In this paper, the dual-optical attention fusion crowd head point counting model (TAPNet) is proposed to address the problem of the difficulty of accurate counting in complex scenes such as crowd dense occlusion and low …
Crowd CountingData AugmentationCrowd counting with crowd attention convolutional neural network
Crowd counting is a challenging problem due to the scene complexity and scale variation. Although deep learning has achieved great improvement in crowd counting, scene complexity affects the judgement of these methods an…
Crowd CountingFew-Shot Scene Adaptive Crowd Counting Using Meta-Learning
We consider the problem of few-shot scene adaptive crowd counting. Given a target camera scene, our goal is to adapt a model to this specific scene with only a few labeled images of that scene. The solution to this probl…
Crowd CountingMeta-LearningAdaCrowd: Unlabeled Scene Adaptation for Crowd Counting
We address the problem of image-based crowd counting. In particular, we propose a new problem called unlabeled scene-adaptive crowd counting. Given a new target scene, we would like to have a crowd counting model specifi…
Crowd Counting