scholarly journals Headcount of the Crowd in a Congested Scene

2021 ◽  
Vol 12 (2) ◽  
pp. 2926-2933
Author(s):  
Mayur Nair, Et. al.
Keyword(s):  
Neural Network ◽  
Scene Recognition ◽  
Research Field ◽  
Crowd Behavior ◽  
Heat Maps ◽  
Single Frame ◽  
Original Distribution ◽  
Crowd Monitoring ◽  
Density Map ◽  
The Given

Crowd Counting and estimation of density is really challenging and an important problem if we visually analyze the crowd. Crowd Monitoring and Analyzing Crowd behavior has been an important aspect for every research field. A lot of already existing approaches use techniques based on regression on heat maps(density) to count people present in from a single frame. These techniques however cannot restrain an individual walking and further cannot approximate the original distribution of pedestrian in the locality. Whereas, detection-based techniques detect and restrain walking men’s in the frame, but the efficiency of these techniques challenged when implemented in high-density crowd situations. To get the better of the limitations of above-mentioned problem, we have used the (Congested Scene Recognition) Neural Network. By using this type of Neural network, we are able to visualize the detection and form density map according to produce accurate outputs for the given scene. The experimental outcomes of the successfully showcases the effectiveness of the approach used.

scholarly journals Convolutional Neural Network for Crowd Counting on Metro Platforms

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 703
Author(s):  
Jun Zhang ◽  
Jiaze Liu ◽  
Zhizhong Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Estimation Error ◽  
Image Features ◽  
Urban Rail Transit ◽  
Crowd Counting ◽  
Passenger Flow ◽  
Urban Rail ◽  
Density Map ◽  
Flow Detection

Owing to the increased use of urban rail transit, the flow of passengers on metro platforms tends to increase sharply during peak periods. Monitoring passenger flow in such areas is important for security-related reasons. In this paper, in order to solve the problem of metro platform passenger flow detection, we propose a CNN (convolutional neural network)-based network called the MP (metro platform)-CNN to accurately count people on metro platforms. The proposed method is composed of three major components: a group of convolutional neural networks is used on the front end to extract image features, a multiscale feature extraction module is used to enhance multiscale features, and transposed convolution is used for upsampling to generate a high-quality density map. Currently, existing crowd-counting datasets do not adequately cover all of the challenging situations considered in this study. Therefore, we collected images from surveillance videos of a metro platform to form a dataset containing 627 images, with 9243 annotated heads. The results of the extensive experiments showed that our method performed well on the self-built dataset and the estimation error was minimum. Moreover, the proposed method could compete with other methods on four standard crowd-counting datasets.

scholarly journals Low-Rank and Sparse Based Deep-Fusion Convolutional Neural Network for Crowd Counting

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Siqi Tang ◽  
Zhisong Pan ◽  
Xingyu Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Regression Method ◽  
Low Rank ◽  
Counting Method ◽  
Direct Integral ◽  
Crowd Counting ◽  
Counting Methods ◽  
Density Map

This paper proposes an accurate crowd counting method based on convolutional neural network and low-rank and sparse structure. To this end, we firstly propose an effective deep-fusion convolutional neural network to promote the density map regression accuracy. Furthermore, we figure out that most of the existing CNN based crowd counting methods obtain overall counting by direct integral of estimated density map, which limits the accuracy of counting. Instead of direct integral, we adopt a regression method based on low-rank and sparse penalty to promote accuracy of the projection from density map to global counting. Experiments demonstrate the importance of such regression process on promoting the crowd counting performance. The proposed low-rank and sparse based deep-fusion convolutional neural network (LFCNN) outperforms existing crowd counting methods and achieves the state-of-the-art performance.

scholarly journals Novel Hopfield Neural Network Model with Election Algorithm for Random 3 Satisfiability

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1292
Author(s):  
Muna Mohammed Bazuhair ◽  
Siti Zulaikha Mohd Jamaludin ◽  
Nur Ezlin Zamri ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Mohd. Asyraf Mansor ◽  
...  
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Hopfield Neural Network ◽  
Higher Order ◽  
Research Field ◽  
Learning Phase ◽  
Logical Rule ◽  
Election Algorithm ◽  
Logical Rules ◽  
Retrieval Phase

One of the influential models in the artificial neural network (ANN) research field for addressing the issue of knowledge in the non-systematic logical rule is Random k Satisfiability. In this context, knowledge structure representation is also the potential application of Random k Satisfiability. Despite many attempts to represent logical rules in a non-systematic structure, previous studies have failed to consider higher-order logical rules. As the amount of information in the logical rule increases, the proposed network is unable to proceed to the retrieval phase, where the behavior of the Random Satisfiability can be observed. This study approaches these issues by proposing higher-order Random k Satisfiability for k ≤ 3 in the Hopfield Neural Network (HNN). In this regard, introducing the 3 Satisfiability logical rule to the existing network increases the synaptic weight dimensions in Lyapunov’s energy function and local field. In this study, we proposed an Election Algorithm (EA) to optimize the learning phase of HNN to compensate for the high computational complexity during the learning phase. This research extensively evaluates the proposed model using various performance metrics. The main findings of this research indicated the compatibility and performance of Random 3 Satisfiability logical representation during the learning and retrieval phase via EA with HNN in terms of error evaluations, energy analysis, similarity indices, and variability measures. The results also emphasized that the proposed Random 3 Satisfiability representation incorporates with EA in HNN is capable to optimize the learning and retrieval phase as compared to the conventional model, which deployed Exhaustive Search (ES).

A multi-branch convolutional neural network with density map for aphid counting

2022 ◽  
Vol 213 ◽  
pp. 148-161
Author(s):  
Rui Li ◽  
Rujing Wang ◽  
Chengjun Xie ◽  
Hongbo Chen ◽  
Qi Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Density Map

scholarly journals An Encoder-Decoder Neural Network for Indefinite Length Digit Sequences in Natural Scene Recognition

2019 ◽  
Vol 1345 ◽  
pp. 022025
Author(s):  
Tong Zhang ◽  
Erhan Zhang ◽  
Hanfeng Zhang ◽  
Feihong Shen ◽  
Dongwei Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Scene Recognition ◽  
Natural Scene

scholarly journals A Green Prospective for Learned Post-processing in Sparse-view Tomographic Reconstruction

2021 ◽  
Author(s):  
Elena Morotti ◽  
Davide Evangelista ◽  
Elena Loli Piccolomini
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Tomographic Reconstruction ◽  
Research Field ◽  
Filtered Backprojection ◽  
Post Processing ◽  
Training Set ◽  
Imaging Reconstruction ◽  
Active Research

Deep Learning is developing interesting tools which are of great interest for inverse imaging applications. In this work, we consider a medical imaging reconstruction task from subsampled measurements, which is an active research field where Convolutional Neural Networks have already revealed their great potential. However, the commonly used architectures are very deep and, hence, prone to overfitting and unfeasible for clinical usages. Inspired by the ideas of the green-AI literature, we here propose a shallow neural network to perform an efficient Learned Post-Processing on images roughly reconstructed by the filtered backprojection algorithm. The results obtained on images from the training set and on unseen images, using both the non-expensive network and the widely used very deep ResUNet show that the proposed network computes images of comparable or higher quality in about one fourth of time.

scholarly journals Speech Enhancement Using Deep Learning Methods: A Review

2021 ◽  
Vol 21 (1) ◽  
pp. 19
Author(s):  
Asri Rizki Yuliani ◽  
M. Faizal Amri ◽  
Endang Suryawati ◽  
Ade Ramdan ◽  
Hilman Ferdinandus Pardede
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Speech Enhancement ◽  
Speech Signal ◽  
Research Field ◽  
Learning Technologies ◽  
Learning Approaches ◽  
Speech Signal Processing ◽  
Generative Adversarial Network ◽  
Advantages And Disadvantages

Speech enhancement, which aims to recover the clean speech of the corrupted signal, plays an important role in the digital speech signal processing. According to the type of degradation and noise in the speech signal, approaches to speech enhancement vary. Thus, the research topic remains challenging in practice, specifically when dealing with highly non-stationary noise and reverberation. Recent advance of deep learning technologies has provided great support for the progress in speech enhancement research field. Deep learning has been known to outperform the statistical model used in the conventional speech enhancement. Hence, it deserves a dedicated survey. In this review, we described the advantages and disadvantages of recent deep learning approaches. We also discussed challenges and trends of this field. From the reviewed works, we concluded that the trend of the deep learning architecture has shifted from the standard deep neural network (DNN) to convolutional neural network (CNN), which can efficiently learn temporal information of speech signal, and generative adversarial network (GAN), that utilize two networks training.

scholarly journals A High-Density Crowd Counting Method Based on Convolutional Feature Fusion

2018 ◽  
Vol 8 (12) ◽  
pp. 2367 ◽  
Author(s):  
Hongling Luo ◽  
Jun Sang ◽  
Weiqun Wu ◽  
Hong Xiang ◽  
Zhili Xiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Feature Fusion ◽  
High Density ◽  
Counting Problem ◽  
Crowd Counting ◽  
Low Level ◽  
Density Map ◽  
High Level

In recent years, the trampling events due to overcrowding have occurred frequently, which leads to the demand for crowd counting under a high-density environment. At present, there are few studies on monitoring crowds in a large-scale crowded environment, while there exists technology drawbacks and a lack of mature systems. Aiming to solve the crowd counting problem with high-density under complex environments, a feature fusion-based deep convolutional neural network method FF-CNN (Feature Fusion of Convolutional Neural Network) was proposed in this paper. The proposed FF-CNN mapped the crowd image to its crowd density map, and then obtained the head count by integration. The geometry adaptive kernels were adopted to generate high-quality density maps which were used as ground truths for network training. The deconvolution technique was used to achieve the fusion of high-level and low-level features to get richer features, and two loss functions, i.e., density map loss and absolute count loss, were used for joint optimization. In order to increase the sample diversity, the original images were cropped with a random cropping method for each iteration. The experimental results of FF-CNN on the ShanghaiTech public dataset showed that the fusion of low-level and high-level features can extract richer features to improve the precision of density map estimation, and further improve the accuracy of crowd counting.

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