COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation

Pham, Viet-Quoc; Kozakaya, Tatsuo; Yamaguchi, Osamu; Okada, Ryuzo

doi:10.1109/iccv.2015.372

Cited by 332 publications

(184 citation statements)

References 23 publications

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“…Earlier methods [4,5,11,28] usually predict the counts directly from the features, which will lead to poor performance as the spatial awareness is completely ignored. Later methods try to estimate the density map for counting [16,26,29], where the crowd count is obtained by integrating all pixel values over the density map. Though learning the density map somewhat provides the spatial information, their models still have difficulties in preserving the high-frequency variation in the density map.…”

Section: Regression-based Methodsmentioning

confidence: 99%

Learning Spatial Awareness to Improve Crowd Counting

Cheng

Dai

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

138

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The aim of crowd counting is to estimate the number of people in images by leveraging the annotation of center positions for pedestrians' heads. Promising progresses have been made with the prevalence of deep Convolutional Neural Networks. Existing methods widely employ the Euclidean distance (i.e., L 2 loss) to optimize the model, which, however, has two main drawbacks: (1) the loss has difficulty in learning the spatial awareness (i.e., the position of head) since it struggles to retain the high-frequency variation in the density map, and (2) the loss is highly sensitive to various noises in crowd counting, such as the zeromean noise, head size changes, and occlusions. Although the Maximum Excess over SubArrays (MESA) loss has been previously proposed by [16] to address the above issues by finding the rectangular subregion whose predicted density map has the maximum difference from the ground truth, it cannot be solved by gradient descent, thus can hardly be integrated into the deep learning framework. In this paper, we present a novel architecture called SPatial Awareness Network (SPANet) to incorporate spatial context for crowd counting. The Maximum Excess over Pixels (MEP) loss is proposed to achieve this by finding the pixel-level subregion with high discrepancy to the ground truth. To this end, we devise a weakly supervised learning scheme to generate such region with a multi-branch architecture. The proposed framework can be integrated into existing deep crowd counting methods and is end-to-end trainable. Extensive experiments on four challenging benchmarks show that our method can significantly improve the performance of baselines. More remarkably, our approach outperforms the state-of-the-art methods on all benchmark datasets.

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Section: Regression-based Methodsmentioning

confidence: 99%

Learning Spatial Awareness to Improve Crowd Counting

Cheng

Dai

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

138

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“…It has two steps: 1) extract powerful image features, 2) use various regression models to estimate the crowd count. Specifically, image features include edge features [9,11,36,45,47] and texture features [10,11,24,43]. Regression methods cover Bayesian [9], Ridge [11], Forest [43] and Markov Random Field [24,41].…”

Section: Regression-based Methodsmentioning

confidence: 99%

Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting

Cheng

Dai

et al. 2019

Proceedings of the 27th ACM International Conference on Multimedia

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Tremendous variation in the scale of people/head size is a critical problem for crowd counting. To improve the scale invariance of feature representation, recent works extensively employ Convolutional Neural Networks with multi-column structures to handle different scales and resolutions. However, due to the substantial redundant parameters in columns, existing multi-column networks invariably exhibit almost the same scale features in different columns, which severely affects counting accuracy and leads to overfitting. In this paper, we attack this problem by proposing a novel Multicolumn Mutual Learning (McML) strategy. It has two main innovations: 1) A statistical network is incorporated into the multi-column framework to estimate the mutual information between columns, which can approximately indicate the scale correlation between features from different columns. By minimizing the mutual information, each column is guided to learn features with different image scales. 2) We devise a mutual learning scheme that can alternately optimize each column while keeping the other columns fixed on each mini-batch training data. With such asynchronous parameter update process, each column is inclined to learn different feature representation from others, which can efficiently reduce the parameter redundancy and improve generalization ability. More remarkably, McML can be applied to all existing multi-column networks and is end-to-end trainable. Extensive experiments on four challenging benchmarks show that McML can significantly improve the original multi-column networks and outperform the other state-of-the-art approaches.

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“…Density estimationbased approaches are therefore developed with the ability to conduct pixel-wise regressions. Linear mapping [12] and non-linear mapping [13] methods were utilized for density calculation successively.…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Deep Network for Crowd Counting

Shi

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Automatic analysis of highly crowded people has attracted extensive attention from computer vision research. Previous approaches for crowd counting have already achieved promising performance across various benchmarks. However, to deal with the real situation, we hope the model run as fast as possible while keeping accuracy. In this paper, we propose a compact convolutional neural network for crowd counting which learns a more efficient model with a small number of parameters. With three parallel filters executing the convolutional operation on the input image simultaneously at the front of the network, our model could achieve nearly realtime speed and save more computing resources. Experiments on two benchmarks show that our proposed method not only takes a balance between performance and efficiency which is more suitable for actual scenes but also is superior to existing light-weight models in speed.

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COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation

Cited by 332 publications

References 23 publications

Learning Spatial Awareness to Improve Crowd Counting

Learning Spatial Awareness to Improve Crowd Counting

Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting

A Real-Time Deep Network for Crowd Counting

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