Reverse Perspective Network for Perspective-Aware Object Counting

Yang, Yifan; Li, Guorong; Wu, Zhe; Su, Li; Huang, Qingming; Sebe, Nicu

doi:10.1109/cvpr42600.2020.00443

Cited by 113 publications

(65 citation statements)

References 29 publications

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“…Liu et al [21] introduced a structured feature enhancement module based on conditional random fields to refine features mutually with a message passing mechanism. [22][23][24][25][26][27][28] proposed data-driven and adaptive methods that can understand highly congested scenes and perform well. Other works [29][30][31][32][33] used the multi-scale networks for image crowd counting, which are both accurate and cost-effective for practical applications.…”

Section: Map-based Methods For Crowd Countingmentioning

confidence: 99%

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MSR‐FAN: Multi‐scale residual feature‐aware network for crowd counting

Zhao

Min

Wei

et al. 2021

IET Image Processing

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Crowd counting aims to count the number of people in crowded scenes, which is important to the security systems, traffic control and so on. The existing methods typically using local features cannot properly handle the perspective distortion and the varying scales in congested scene images, and henceforth perform wrong people counting. To alleviate this issue, this study proposes a multi-scale residual feature-aware network (MSR-FAN) that combines multi-scale features using multiple receptive field sizes and learns the featureaware information on each image. The MSR-FAN is trained end-to-end to generate highquality density map and evaluate the crowd number. The method consists of three parts. To handle the perspective changes problem, the first part, the direction-based featureenhanced network, is designed to encode the perspective information in four directions based on the initial image feature. The second part, the proposed multi-scale residual block module, gets the global information to handle the represent the regional feature better. This module explores features of different scales as well as reinforce the global feature. The third part, the feature-aware block, is designed to extract the feature hidden in the different channels. Experiment results based on benchmark datasets show that the proposed approach outperforms the existing state-of-the-art methods.

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Section: Map-based Methods For Crowd Countingmentioning

confidence: 99%

“…This dataset is mainly collected in the outside scenes, which is less intrusive and has less traffic. This paper compared with [8,22,23,34] and the experiment results show that the proposed MSR-FAN can handle this kind of simpler dataset. It can be found that the MAE value of ours is close to [34].…”

Section: Comparisons With State-of-the-artmentioning

confidence: 99%

MSR‐FAN: Multi‐scale residual feature‐aware network for crowd counting

Zhao

Min

Wei

et al. 2021

IET Image Processing

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“…CNN-based counting. With the development of deep learning [11][12][13][14], the CNN-based methods [15][16][17][18] can transform the highly congested images into the densityestimation problem. The point-level labeling is adopted to annotate the objects, and the density map is generated following the Gaussian filter [17].…”

Section: Related Workmentioning

confidence: 99%

“…For example, the Multi-column CNN (MCNN) [15] was put forward when using the cascade architecture of the CNN to regress the density map. The reverse perspective network (RPN) [16] was designed by using perspective estimators and coordinate transformers through the meta-learning for accomplishing the crowd counting task. The CODA [17] provided the alternative way which uses adversarial learning to match the predicted density map and the ground truth density map.…”

Section: Related Workmentioning

confidence: 99%

AVMSN: An Audio-Visual Two Stream Crowd Counting Framework Under Low-Quality Conditions

Xie

et al. 2021

IEEE Access

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“…Some advanced methods utilize extra semantic segmentation tasks to learn crowd localization information with complicated pipelines [19,11].…”

Section: Guidance Branchmentioning

confidence: 99%

Lightweight Dual-Task Networks For Crowd Counting In Aerial Images

Tian

Duan

Zhang

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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As a research hotspot of computer vision, crowd counting methods have achieved success in natural images. But crowd counting in aerial images are rarely explored, and existing methods do not perform well because of the higher resolution, smaller object scale and more complex scene. Therefore, this paper proposes a lightweight dual-task network (LDNet) for crowd counting, which only uses bifurcated structure to overcome these new challenges in aerial images without complicated pipelines. To realize this, a complete but efficient Guidance Branch is proposed to assist Counting Branch in fitting crowd distribution. Furthermore, a scene attention mechanism is used to consider the complex scene information, which are never considered by existing methods. Our LD-Net outperforms existing methods on aerial crowd counting dataset (Visdrone), and gets better or comparable results on natural crowd counting datasets (UCF CC 50, UCF QNRF, ShanghaiTech Part A).

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Reverse Perspective Network for Perspective-Aware Object Counting

Cited by 113 publications

References 29 publications

MSR‐FAN: Multi‐scale residual feature‐aware network for crowd counting

MSR‐FAN: Multi‐scale residual feature‐aware network for crowd counting

AVMSN: An Audio-Visual Two Stream Crowd Counting Framework Under Low-Quality Conditions

Lightweight Dual-Task Networks For Crowd Counting In Aerial Images

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