Locating and Counting Heads in Crowds With a Depth Prior

Lian, Dongze; Chen, Xianing; Li, Jing; Luo, Weixin; Gao, Shenghua

doi:10.1109/tpami.2021.3124956

Cited by 31 publications

(7 citation statements)

References 79 publications

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“…Since there are few RGBD crowd counting datasets currently, there is not much work to complete crowd counting based on RGBD images [54,55]. In these studies, depth information usually provides prior knowledge of head position for RGB image segmentation.…”

Section: Related Workmentioning

confidence: 99%

Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis

Ren

2022

Journal of Sensors

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Objective. It has become a very difficult task for cameras to complete real-time crowd counting under congestion conditions. Methods. This paper proposes a DRC-ConvLSTM network, which combines a depth-aware model and depth-adaptive Gaussian kernel to extract the spatial-temporal features and depth-level matching of crowd depth space edge constraints in videos, and finally achieves satisfactory crowd density estimation results. The model is trained with weak supervision on a training set of point-labeled images. The design of the detector is to propose a deep adaptive perception network DRD-NET, which can better initialize the size and position of the head detection frame in the image with the help of density map and RGBD-adaptive perception network. Results. The results show that our method achieves the best performance in RGBD dense video crowd counting on five labeled sequence datasets; the MICC dataset, CrowdFlow dataset, FDST dataset, Mall dataset, and UCSD dataset were evaluated to verify its effectiveness. Conclusion. The experimental results show that the proposed DRD-NET model combined with DRC-ConvLSTM outperforms the existing video crowd counting ConvLSTM model, and the effectiveness of the parameters of each part of the model is further proved by ablation experiments.

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Section: Related Workmentioning

confidence: 99%

Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis

Ren

2022

Journal of Sensors

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“…Then, in order to create the dual attention map, we aggregated the FoV attention map and depth attention map as given in (7). After that, to enhanced the saliency estimation we aggregated this dual attention map with hypothetical gaze distribution G (object channel) to create hypo dual attention map as in (8), where ⊗ denotes the element wise product.…”

Section: ) Depth Range Selectormentioning

confidence: 99%

“…Several solutions have introduced for customer behaviour analyzis in retail using the rapid developments in computer vision technology. For instance, counting the number of people and detecting the hot spots in retail [6] and public [7], and tracking shoppers' emotion [5] are such applications. However, the existing solutions only captures coarse touch-points of a shopper's journey and vulnerable to unconstrained environment settings.…”

Section: Introductionmentioning

confidence: 99%

Customer Gaze Estimation in Retail Using Deep Learning

et al. 2022

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At present, intelligent computing applications are widely used in different domains, including retail stores. The analysis of customer behaviour has become crucial for the benefit of both customers and retailers. In this regard, the novel concept of remote gaze estimation using deep learning has shown promising results in analyzing customer behaviour in retail due to its scalability, robustness, low cost, and uninterrupted nature. This study presents a three-stage, three-attention-based deep convolutional neural network for remote gaze estimation in retail using only image data. In the first stage, we design a mechanism to estimate the 3D gaze of the subject using image data and monocular depth estimation. The second stage presents a novel three-attention mechanism to estimate the gaze in the wild from field-of-view, depth range, and object channel attentions. The third stage generates the gaze saliency heatmap from the output attention map of the second stage. We train and evaluate the proposed model on the benchmark GOO-Real dataset and compare the results with baseline models. Further, we adapt our model to real-retail environments by introducing a novel Retail Gaze dataset. Extensive experiments demonstrate that our approach significantly improves remote gaze target estimation performance on GOO-Real and Retail Gaze datasets. INDEX TERMS Computer vision, deep learning, gaze estimation, retail customer behaviour

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“…Song et al [53] use the Kinect sensor to obtain depth images and propose a depth region suggestion network for counting. Lian et al [54], [55] propose a strategy to generate crowd depth-adaptive density maps, and a real-world RGB-D crowd counting dataset called Shang-haiTechRGBD and a synthetic dataset ShanghaiTechRGBDsyn are collected. Yang et al [56] propose a Bidirectional Cross-modal Attention (BCA) mechanism to focus on crowded regions in images though depth information.…”

Section: B Multi-modal Crowd Countingmentioning

confidence: 99%

MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting

Chen¹,

Ji²,

Yuan³

et al. 2022

Preprint

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RGB-Thermal (RGB-T) crowd counting is a challenging task, which uses thermal images as complementary information to RGB images to deal with the decreased performance of unimodal RGB-based methods in scenes with low-illumination or similar backgrounds. Most existing methods propose welldesigned structures for cross-modal fusion in RGB-T crowd counting. However, these methods have difficulty in encoding cross-modal contextual semantic information in RGB-T image pairs. Considering the aforementioned problem, we propose a two-stream RGB-T crowd counting network called Multi-Attention Fusion Network (MAFNet), which aims to fully capture long-range contextual information from the RGB and thermal modalities based on the attention mechanism. Specifically, in the encoder part, a Multi-Attention Fusion (MAF) module is embedded into different stages of the two modality-specific branches for cross-modal fusion at the global level. In addition, a Multi-modal Multi-scale Aggregation (MMA) regression head is introduced to make full use of the multi-scale and contextual information across modalities to generate high-quality crowd density maps. Extensive experiments on two popular datasets show that the proposed MAFNet is effective for RGB-T crowd counting and achieves the state-of-the-art performance.

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Locating and Counting Heads in Crowds With a Depth Prior

Cited by 31 publications

References 79 publications

Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis

Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis

Customer Gaze Estimation in Retail Using Deep Learning

MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting

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