Background Noise Filtering and Distribution Dividing for Crowd Counting

Hong, Min-Cheol; Ren, Wenqi; Xiong, Yuan; Pan, Xiaoqi; Zhou, Zhong; Cao, Xiaochun; Wu, Wei

doi:10.1109/tip.2020.3009030

Cited by 24 publications

(6 citation statements)

References 44 publications

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“…Most of the methods utilized the potential of a model itself with auxiliary tasks, such as object detection, crowd segmentation, density level classification, etc., to enhance the feature tuning for density map regression. For example, the task of patch-based density level classification [7], [54], [55], [9], [56], [57], [58] can enhance patchlevel density level information, which helped to address the underestimation and the overestimation problems of density map regressions. However, it may be difficult to guide the pixel-wise density map regression via patch-wise density level classification because of the gap between pixel-level and patch-level feature learning.…”

Section: B Auxiliary Tasks Based Countingmentioning

confidence: 99%

Counting with Adaptive Auxiliary Learning

Meng¹,

Bridge²,

Wang³

et al. 2022

Preprint

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This paper proposes an adaptive auxiliary task learning based approach for object counting problems. Unlike existing auxiliary task learning based methods, we develop an attention-enhanced adaptively shared backbone network to enable both task-shared and task-tailored features learning in an end-to-end manner. The network seamlessly combines standard Convolution Neural Network (CNN) and Graph Convolution Network (GCN) for feature extraction and feature reasoning among different domains of tasks. Our approach gains enriched contextual information by iteratively and hierarchically fusing the features across different task branches of the adaptive CNN backbone. The whole framework pays special attention to the objects' spatial locations and varied density levels, informed by object (or crowd) segmentation and density level segmentation auxiliary tasks. In particular, thanks to the proposed dilated contrastive density loss function, our network benefits from individual and regional context supervision in terms of pixel-independent and pixel-dependent feature learning mechanisms, along with strengthened robustness. Experiments on seven challenging multidomain datasets demonstrate that our method achieves superior performance to the state-of-the-art auxiliary task learning based counting methods. Our code is made publicly available at: https: //github.com/smallmax00/Counting With Adaptive Auxiliary

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Section: B Auxiliary Tasks Based Countingmentioning

confidence: 99%

Counting with Adaptive Auxiliary Learning

Meng¹,

Bridge²,

Wang³

et al. 2022

Preprint

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show abstract

“…The Capon approach estimates the AoA by reordering terms in (1) and stacking the received signal from all receiver channels in a column vector. It can be expressed as: (10) Here, θ is the angle of arrival (AoA) of the target, Γ depends on the channel gain/phase mismatches, and a depends on AoA, which is called the steering vector. If there is more than one target at different ranges, i.e., with different fb, then the received vector is the summation of all the vectors received from each target given by (11) where A is a matrix with K number of targets that has columns corresponding to the steering vector of each target.…”

Section: A Mim0 Fmcw Radar Conceptmentioning

confidence: 99%

“…Camera vision [7], [8] and infrared radiation (IR) [9] have been widely used for people counting and human detection in various applications. The most common application is in large environments such as homes and shopping malls [10]. Moreover, near-infrared (NIR) is commonly used in transportation imaging systems for vehicle occupancy detection, seatbelt violation and cell phone usage detection [11].…”

Section: Introductionmentioning

confidence: 99%

AI-Powered In-Vehicle Passenger Monitoring Using Low-Cost mm-Wave Radar

et al. 2022

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We propose a novel algorithm to identify occupied seats, i.e., the number of occupants and their positions, using a frequency modulated continuous wave radar. Instead of using a high-resolution radar, which increases the cost and area, and performing complex signal processing with several variables to be tuned for each scenario, we integrate machine learning algorithms with a low-cost radar system. Based on heat maps obtained from the Capon beamformer, we train a machine classifier to predict the number of occupants and their positions in a vehicle. We follow two different classification methods: multiclass classification and binary classification. We compare three classifiers, support vector machine (SVM), K-Nearest Neighbors (KNN) and Random Forest (RF), in terms of accuracy and computational complexity for both testing and training sets. Our proposed system using an SVM classifier achieved an overall accuracy of 97% in finding the defined scenarios in both multiclass classification and binary classification methods. In addition, to show the effectiveness of our proposed in-vehicle occupancy detection method, we provide the results of a common group tracking and people counting method of occupancy detection. Compared to the common method, the effectiveness, robustness, and accuracy of our proposed in-vehicle occupancy detection method are shown.

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“…Following this work, Sadler et al [ 41 ] used random forests to regress crowd density, and the training efficiency was also greatly improved. In [ 42 ], Mo mentioned a response of a Laws filter convolved with mask to obtain a two-dimensional density layer and finally realized the regression of difficult-to-distinguish crowd images, where mask is to create a mask by the gray-scale restricted area growth method. In other words, methods based on these regressions are more likely to fail in crowd counting in image areas with high crowd density due to the lack of deeper features.…”

Section: Related Workmentioning

confidence: 99%

Enhancement of Local Crowd Location and Count: Multiscale Counting Guided by Head RGB-Mask

Ren

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Background. In crowded crowd images, traditional detection models often have the problems of inaccurate multiscale target count and low recall rate. Methods. In order to solve the above two problems, this paper proposes an MLP-CNN model, which combined with FPN feature pyramid can fuse the feature map of low-resolution and high-resolution semantic information with less computation and can effectively solve the problem of inaccurate head count of multiscale people. MLP-CNN “mid-term” fusion model can effectively fuse the features of RGB head image and RGB-Mask image. With the help of head RGB-Mask annotation and adaptive Gaussian kernel regression, the enhanced density map can be generated, which can effectively solve the problem of low recall of head detection. Results. MLP-CNN model was applied in ShanghaiTech and UCF_ CC_ 50 and UCF-QNRF. The test results show that the error of the method proposed in this paper has been significantly improved, and the recall rate can reach 79.91%. Conclusion. MLP-CNN model not only improves the accuracy of population counting in density map regression, but also improves the detection rate of multiscale population head targets.

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Background Noise Filtering and Distribution Dividing for Crowd Counting

Cited by 24 publications

References 44 publications

Counting with Adaptive Auxiliary Learning

Counting with Adaptive Auxiliary Learning

AI-Powered In-Vehicle Passenger Monitoring Using Low-Cost mm-Wave Radar

Enhancement of Local Crowd Location and Count: Multiscale Counting Guided by Head RGB-Mask

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