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

Ilyas, Naveed; Shahzad, Ahsan; Kim, Kiseon

doi:10.3390/s20010043

Cited by 70 publications

(28 citation statements)

References 133 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the controller node, the pedestrian flow estimation block estimates pedestrian density at each cell. This block is assumed to be built on commercially available pedestrian counting technology [ 26 , 27 , 28 , 29 ]. Here, two main candidates emerge to be deployed in real implementations of CellEVAC: Time of Flight People ( ) counting technology, which is based on signal reflection, or Thermal cameras.…”

Section: Methodsmentioning

confidence: 99%

LED Wristbands for Cell-Based Crowd Evacuation: An Adaptive Exit-Choice Guidance System Architecture

López-Carmona

García

2020

Sensors

View full text Add to dashboard Cite

Cell-based crowd evacuation systems provide adaptive or static exit-choice indications that favor a coordinated group dynamic, improving evacuation time and safety. While a great effort has been made to modeling its control logic by assuming an ideal communication and positioning infrastructure, the architectural dimension and the influence of pedestrian positioning uncertainty have been largely overlooked. In our previous research, a cell-based crowd evacuation system (CellEVAC) was proposed that dynamically allocates exit gates to pedestrians in a cell-based pedestrian positioning infrastructure. This system provides optimal exit-choice indications through color-based indications and a control logic module built upon an optimized discrete-choice model. Here, we investigate how location-aware technologies and wearable devices can be used for a realistic deployment of CellEVAC. We consider a simulated real evacuation scenario (Madrid Arena) and propose a system architecture for CellEVAC that includes: a controller node, a radio-controlled light-emitting diode (LED) wristband subsystem, and a cell-node network equipped with active Radio Frequency Identification (RFID) devices. These subsystems coordinate to provide control, display, and positioning capabilities. We quantitatively study the sensitivity of evacuation time and safety to uncertainty in the positioning system. Results showed that CellEVAC was operational within a limited range of positioning uncertainty. Further analyses revealed that reprogramming the control logic module through a simulation optimization process, simulating the positioning system’s expected uncertainty level, improved the CellEVAC performance in scenarios with poor positioning systems.

show abstract

Section: Methodsmentioning

confidence: 99%

LED Wristbands for Cell-Based Crowd Evacuation: An Adaptive Exit-Choice Guidance System Architecture

López-Carmona

García

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Additionally, it is known as deep neural learning or deep neural networks. Convolution neural networks (CNNs) are a subset of deep neural networks, and have attracted a lot of attention in recent years and are used in image recognition [27][28][29]. They are often used to extract features and identify the surrounding environment to build a deep network.…”

Section: The Deep Neural Network (Dnn)mentioning

confidence: 99%

Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks

et al. 2021

View full text Add to dashboard Cite

Anesthesia assessment is most important during surgery. Anesthesiologists use electrocardiogram (ECG) signals to assess the patient’s condition and give appropriate medications. However, it is not easy to interpret the ECG signals. Even physicians with more than 10 years of clinical experience may still misjudge. Therefore, this study uses convolutional neural networks to classify ECG image types to assist in anesthesia assessment. The research uses Internet of Things (IoT) technology to develop ECG signal measurement prototypes. At the same time, it classifies signal types through deep neural networks, divided into QRS widening, sinus rhythm, ST depression, and ST elevation. Three models, ResNet, AlexNet, and SqueezeNet, are developed with 50% of the training set and test set. Finally, the accuracy and kappa statistics of ResNet, AlexNet, and SqueezeNet in ECG waveform classification were (0.97, 0.96), (0.96, 0.95), and (0.75, 0.67), respectively. This research shows that it is feasible to measure ECG in real time through IoT and then distinguish four types through deep neural network models. In the future, more types of ECG images will be added, which can improve the real-time classification practicality of the deep model.

show abstract

“…CNNs have significant ability to learn deeper and powerful features. Existing CNN-based crowd counting (CC) techniques enhance the counting accuracy by using well-known networks such as multi-column, multi-tasking, dilated, and de-convolutional [ 3 ] networks. These networks have been widely used individually or in combination with each other to increase the performance at the cost of major shortcomings, such as large amounts of training time, ineffective branch structure, sparse pixel sampling rates, information loss, and extraction of irrelevant information.…”

Section: Introductionmentioning

confidence: 99%

HADF-Crowd: A Hierarchical Attention-Based Dense Feature Extraction Network for Single-Image Crowd Counting

Ilyas

Lee

Kim

2021

Sensors

Self Cite

View full text Add to dashboard Cite

Crowd counting is a challenging task due to large perspective, density, and scale variations. CNN-based crowd counting techniques have achieved significant performance in sparse to dense environments. However, crowd counting in high perspective-varying scenes (images) is getting harder due to different density levels occupied by the same number of pixels. In this way large variations for objects in the same spatial area make it difficult to count accurately. Further, existing CNN-based crowd counting methods are used to extract rich deep features; however, these features are used locally and disseminated while propagating through intermediate layers. This results in high counting errors, especially in dense and high perspective-variation scenes. Further, class-specific responses along channel dimensions are underestimated. To address these above mentioned issues, we therefore propose a CNN-based dense feature extraction network for accurate crowd counting. Our proposed model comprises three main modules: (1) backbone network, (2) dense feature extraction modules (DFEMs), and (3) channel attention module (CAM). The backbone network is used to obtain general features with strong transfer learning ability. The DFEM is composed of multiple sub-modules called dense stacked convolution modules (DSCMs), densely connected with each other. In this way features extracted from lower and middle-lower layers are propagated to higher layers through dense connections. In addition, combinations of task independent general features obtained by the former modules and task-specific features obtained by later ones are incorporated to obtain high counting accuracy in large perspective-varying scenes. Further, to exploit the class-specific response between background and foreground, CAM is incorporated at the end to obtain high-level features along channel dimensions for better counting accuracy. Moreover, we have evaluated the proposed method on three well known datasets: Shanghaitech (Part-A), Shanghaitech (Part-B), and Venice. The performance of the proposed technique justifies its relative effectiveness in terms of selected performance compared to state-of-the-art techniques.

show abstract

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

Cited by 70 publications

References 133 publications

LED Wristbands for Cell-Based Crowd Evacuation: An Adaptive Exit-Choice Guidance System Architecture

LED Wristbands for Cell-Based Crowd Evacuation: An Adaptive Exit-Choice Guidance System Architecture

Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks

HADF-Crowd: A Hierarchical Attention-Based Dense Feature Extraction Network for Single-Image Crowd Counting

Contact Info

Product

Resources

About