Counting challenging crowds robustly using a multi-column multi-task convolutional neural network

Yang, Biao; Cao, Jinmeng; Wang, Nan; Zhang, Yuyu; Zou, Ling

doi:10.1016/j.image.2018.03.004

Cited by 29 publications

(35 citation statements)

References 26 publications

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“…These multitasks involved the extraction of rich semantic-feature information, mapping the input scene image to the semantic scene model (body-part map and structured density map), and crowd counting. Yang et al [96] proposed a multicolumn multitask neural network (MMCNN) to overcome drastic scale variation in an image. They used the multicolumn by incorporating three main changes.…”

Section: Multitask-cnn-cc Techniquesmentioning

confidence: 99%

“…The reason was the consideration of a self-supervised learning technique (increased training-data size). Further, the nMAE of [97] was relatively low when compared to that of [95,96] on the STA and STB datasets due to the above-mentioned reasons. Further, [96] had a relatively low nMAE when compared to that of [94,95] on the UCF and STB datasets.…”

mentioning

confidence: 95%

“…(2) The coherence issue among different density maps is not properly addressed (summing the individual local counts may not be necessary to approximate the total count). Figure 7d depicts that the nMAE of [97] was relatively low when compared to that of [94][95][96] on the UCF dataset. The reason was the consideration of a self-supervised learning technique (increased training-data size).…”

mentioning

confidence: 99%

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Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

Ilyas

Shahzad

Kim

2019

Sensors

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Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive monitoring, identification/recognition, and the management of diverse crowd gatherings can improve many crowd-management-related tasks in terms of efficiency, capacity, reliability, and safety. Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd counting and analysis. In this article, we review, categorize, analyze (limitations and distinctive features), and provide a detailed performance evaluation of the latest convolutional-neural-network-based crowd-counting techniques. We also highlight the potential applications of convolutional-neural-network-based crowd-counting techniques. Finally, we conclude this article by presenting our key observations, providing strong foundation for future research directions while designing convolutional-neural-network-based crowd-counting techniques. Further, the article discusses new advancements toward understanding crowd counting in smart cities using the Internet of Things (IoT).

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Section: Multitask-cnn-cc Techniquesmentioning

confidence: 99%

mentioning

confidence: 95%

mentioning

confidence: 99%

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Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

Ilyas

Shahzad

Kim

2019

Sensors

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“…By producing the information of high-density, the utmost of research concentrated on an approximation of a density map to obtain the total number of the individuals [30]. These approaches were totally depending on the forms of features while CNN was utilized to obtain the valuable info from input automatically and also very efficiently to deal with overlapping among objects, non-uniform lighting, and altering scales [30]. However, the current CNN approaches may err in resolving the two issues of non-stable density distributions and dissimilarity in scale.…”

Section: Introductionmentioning

confidence: 99%

“…However, the current CNN approaches may err in resolving the two issues of non-stable density distributions and dissimilarity in scale. The letter of [30] suggested the multi-column multi-task convolutional neural network (MMCNN) to solve these problems. This paper suggested three innovative methods, namely to offer a new density map which could focus on location and full data, propose a multi-column CNN to get beneficial data from different scales and lastly, to estimate the density map, level of crowded environment, as well as background or foreground mask.…”

Section: Introductionmentioning

confidence: 99%

A Recent Trend in Individual Counting Approach Using Deep Network

Ghazvini¹,

Abdullah²,

Ayob³

2019

IJIMAI

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In video surveillance scheme, counting individuals is regarded as a crucial task. Of all the individual counting techniques in existence, the regression technique can offer enhanced performance under overcrowded area. However, this technique is unable to specify the details of counting individual such that it fails in locating the individual. On contrary, the density map approach is very effective to overcome the counting problems in various situations such as heavy overlapping and low resolution. Nevertheless, this approach may break down in cases when only the heads of individuals appear in video scenes, and it is also restricted to the feature's types. The popular technique to obtain the pertinent information automatically is Convolutional Neural Network (CNN). However, the CNN based counting scheme is unable to sufficiently tackle three difficulties, namely, distributions of non-uniform density, changes of scale and variation of drastic scale. In this study, we cater a review on current counting techniques which are in correlation with deep net in different applications of crowded scene. The goal of this work is to specify the effectiveness of CNN applied on popular individuals counting approaches for attaining higher precision results.

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