BBA-net: A bi-branch attention network for crowd counting

• URL: http://arxiv.org/abs/2201.08983v1
• Date: Sat, 22 Jan 2022 07:30:52 GMT
• Title: BBA-net: A bi-branch attention network for crowd counting
• Authors: Yi Hou, Chengyang Li, Fan Yang, Cong Ma, Liping Zhu, Yuan Li, Huizhu Jia, Xiaodong Xie
• Abstract summary: Current CNN-based regression methods simply extract the density information of pedestrians without finding the position of each person. We propose a Bi-Branch Attention Network (BBA-NET) for crowd counting, which has three innovation points. Our method achieves a lower crowd counting error compared to other state-of-the-art methods.
• Score: 25.82337203107288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the field of crowd counting, the current mainstream CNN-based regression methods simply extract the density information of pedestrians without finding the position of each person. This makes the output of the network often found to contain incorrect responses, which may erroneously estimate the total number and not conducive to the interpretation of the algorithm. To this end, we propose a Bi-Branch Attention Network (BBA-NET) for crowd counting, which has three innovation points. i) A two-branch architecture is used to estimate the density information and location information separately. ii) Attention mechanism is used to facilitate feature extraction, which can reduce false responses. iii) A new density map generation method combining geometric adaptation and Voronoi split is introduced. Our method can integrate the pedestrian's head and body information to enhance the feature expression ability of the density map. Extensive experiments performed on two public datasets show that our method achieves a lower crowd counting error compared to other state-of-the-art methods.

Related papers

• Diffusion-based Data Augmentation for Object Counting Problems [62.63346162144445]
  We develop a pipeline that utilizes a diffusion model to generate extensive training data. We are the first to generate images conditioned on a location dot map with a diffusion model. Our proposed counting loss for the diffusion model effectively minimizes the discrepancies between the location dot map and the crowd images generated.
  arXiv  Detail & Related papers  (2024-01-25T07:28:22Z)
• Semi-supervised Crowd Counting via Density Agency [57.3635501421658]
  We build a learnable auxiliary structure, namely the density agency to bring the recognized foreground regional features close to corresponding density sub-classes. Second, we propose a density-guided contrastive learning loss to consolidate the backbone feature extractor. Third, we build a regression head by using a transformer structure to refine the foreground features further.
  arXiv  Detail & Related papers  (2022-09-07T06:34:00Z)
• Redesigning Multi-Scale Neural Network for Crowd Counting [68.674652984003]
  We introduce a hierarchical mixture of density experts, which hierarchically merges multi-scale density maps for crowd counting. Within the hierarchical structure, an expert competition and collaboration scheme is presented to encourage contributions from all scales. Experiments show that our method achieves the state-of-the-art performance on five public datasets.
  arXiv  Detail & Related papers  (2022-08-04T21:49:29Z)
• Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training [17.6179873429447]
  State-of-the-art semi-supervised semantic segmentation networks with consistency training can help to deal with this issue. We propose to integrate the consistency of both features and outputs in the end-to-end network training of unlabeled samples. Experimental results show that the proposed approach can well extract more complete building structures.
  arXiv  Detail & Related papers  (2022-05-17T14:55:13Z)
• Crowd counting with segmentation attention convolutional neural network [20.315829094519128]
  We propose a novel convolutional neural network architecture called SegCrowdNet. SegCrowdNet adaptively highlights the human head region and suppresses the non-head region by segmentation. SegCrowdNet achieves excellent performance compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-15T08:40:38Z)
• Cascaded Residual Density Network for Crowd Counting [63.714719914701014]
  We propose a novel Cascaded Residual Density Network (CRDNet) in a coarse-to-fine approach to generate the high-quality density map for crowd counting more accurately. A novel additional local count loss is presented to refine the accuracy of crowd counting.
  arXiv  Detail & Related papers  (2021-07-29T03:07:11Z)
• A Strong Baseline for Crowd Counting and Unsupervised People Localization [2.690502103971799]
  We explore a strong baseline for crowd counting and an unsupervised people localization algorithm based on estimated density maps. We collect different backbones and training tricks and evaluate the impact of changing them. We propose a clustering algorithm named isolated KMeans to locate the heads in density maps.
  arXiv  Detail & Related papers  (2020-11-07T08:29:03Z)
• Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks [50.78037828213118]
  This paper tackles the semi-supervised crowd counting problem from the perspective of feature learning. We propose a novel semi-supervised crowd counting method which is built upon two innovative components.
  arXiv  Detail & Related papers  (2020-07-07T05:30:53Z)
• JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method [92.15895515035795]
  We introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++) that contains "4,372" images with "1.51 million" annotations. We propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation.
  arXiv  Detail & Related papers  (2020-04-07T14:59:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.