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Journal articles on the topic 'Crowd dataset'

Author: Grafiati

Published: 11 March 2023

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1

Bhuiyan, Roman, Junaidi Abdullah, Noramiza Hashim, et al. "Deep Dilated Convolutional Neural Network for Crowd Density Image Classification with Dataset Augmentation for Hajj Pilgrimage." Sensors 22, no. 14 (2022): 5102. http://dx.doi.org/10.3390/s22145102.

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Almost two million Muslim pilgrims from all around the globe visit Mecca each year to conduct Hajj. Each year, the number of pilgrims grows, creating worries about how to handle such large crowds and avoid unpleasant accidents or crowd congestion catastrophes. In this paper, we introduced deep Hajj crowd dilated convolutional neural network (DHCDCNNet) for crowd density analysis. This research also presents augmentation technique to create additional dataset based on the hajj pilgrimage scenario. We utilized a single framework to extract both high-level and low-level features. For creating add

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Bhuiyan, Md Roman, Junaidi Abdullah, Noramiza Hashim, et al. "A deep crowd density classification model for Hajj pilgrimage using fully convolutional neural network." PeerJ Computer Science 8 (March 25, 2022): e895. http://dx.doi.org/10.7717/peerj-cs.895.

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This research enhances crowd analysis by focusing on excessive crowd analysis and crowd density predictions for Hajj and Umrah pilgrimages. Crowd analysis usually analyzes the number of objects within an image or a frame in the videos and is regularly solved by estimating the density generated from the object location annotations. However, it suffers from low accuracy when the crowd is far away from the surveillance camera. This research proposes an approach to overcome the problem of estimating crowd density taken by a surveillance camera at a distance. The proposed approach employs a fully c

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Alafif, Tarik, Anas Hadi, Manal Allahyani, et al. "Hybrid Classifiers for Spatio-Temporal Abnormal Behavior Detection, Tracking, and Recognition in Massive Hajj Crowds." Electronics 12, no. 5 (2023): 1165. http://dx.doi.org/10.3390/electronics12051165.

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Individual abnormal behaviors vary depending on crowd sizes, contexts, and scenes. Challenges such as partial occlusions, blurring, a large number of abnormal behaviors, and camera viewing occur in large-scale crowds when detecting, tracking, and recognizing individuals with abnormalities. In this paper, our contribution is two-fold. First, we introduce an annotated and labeled large-scale crowd abnormal behavior Hajj dataset, HAJJv2. Second, we propose two methods of hybrid convolutional neural networks (CNNs) and random forests (RFs) to detect and recognize spatio-temporal abnormal behaviors

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Ren, Guoyin, Xiaoqi Lu, and Yuhao Li. "Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis." Journal of Sensors 2022 (August 12, 2022): 1–19. http://dx.doi.org/10.1155/2022/7247757.

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Objective. It has become a very difficult task for cameras to complete real-time crowd counting under congestion conditions. Methods. This paper proposes a DRC-ConvLSTM network, which combines a depth-aware model and depth-adaptive Gaussian kernel to extract the spatial-temporal features and depth-level matching of crowd depth space edge constraints in videos, and finally achieves satisfactory crowd density estimation results. The model is trained with weak supervision on a training set of point-labeled images. The design of the detector is to propose a deep adaptive perception network DRD-NET

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BHUIYAN, MD ROMAN, Dr Junaidi Abdullah, Dr Noramiza Hashim, et al. "Crowd density estimation using deep learning for Hajj pilgrimage video analytics." F1000Research 10 (January 14, 2022): 1190. http://dx.doi.org/10.12688/f1000research.73156.2.

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Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This research proposes an algorithm based on a Convolutional Neural Network

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BHUIYAN, MD ROMAN, Dr Junaidi Abdullah, Dr Noramiza Hashim, et al. "Crowd density estimation using deep learning for Hajj pilgrimage video analytics." F1000Research 10 (November 24, 2021): 1190. http://dx.doi.org/10.12688/f1000research.73156.1.

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Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This paper aims to propose an algorithm based on a Convolutional Neural Net

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Wu, Junfeng, Zhiyang Li, Wenyu Qu, and Yizhi Zhou. "One Shot Crowd Counting with Deep Scale Adaptive Neural Network." Electronics 8, no. 6 (2019): 701. http://dx.doi.org/10.3390/electronics8060701.

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This paper aims to utilize the deep learning architecture to break through the limitations of camera perspective, image background, uneven crowd density distribution and pedestrian occlusion to estimate crowd density accurately. In this paper, we proposed a new neural network called Deep Scale-Adaptive Convolutional Neural Network (DSA-CNN), which can convert a single crowd image to density map for crowd counting directly. For a crowd image with any size and resolution, our algorithm can output the density map of the crowd image by end-to-end method and finally estimate the number of the crowd

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Kaya, Abdil, Stijn Denis, Ben Bellekens, Maarten Weyn, and Rafael Berkvens. "Large-Scale Dataset for Radio Frequency-Based Device-Free Crowd Estimation." Data 5, no. 2 (2020): 52. http://dx.doi.org/10.3390/data5020052.

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Organisers of events attracting many people have the important task to ensure the safety of the crowd on their venue premises. Measuring the size of the crowd is a critical first step, but often challenging because of occlusions, noise and the dynamics of the crowd. We have been working on a passive Radio Frequency (RF) sensing technique for crowd size estimation, and we now present three datasets of measurements collected at the Tomorrowland music festival in environments containing thousands of people. All datasets have reference data, either based on payment transactions or an access contro

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Shao, Yanhua, Wenfeng Li, Hongyu Chu, Zhiyuan Chang, Xiaoqiang Zhang, and Huayi Zhan. "A Multitask Cascading CNN with MultiScale Infrared Optical Flow Feature Fusion-Based Abnormal Crowd Behavior Monitoring UAV." Sensors 20, no. 19 (2020): 5550. http://dx.doi.org/10.3390/s20195550.

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Visual-based object detection and understanding is an important problem in computer vision and signal processing. Due to their advantages of high mobility and easy deployment, unmanned aerial vehicles (UAV) have become a flexible monitoring platform in recent years. However, visible-light-based methods are often greatly influenced by the environment. As a result, a single type of feature derived from aerial monitoring videos is often insufficient to characterize variations among different abnormal crowd behaviors. To address this, we propose combining two types of features to better represent

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Zhang, Cong, Kai Kang, Hongsheng Li, Xiaogang Wang, Rong Xie, and Xiaokang Yang. "Data-Driven Crowd Understanding: A Baseline for a Large-Scale Crowd Dataset." IEEE Transactions on Multimedia 18, no. 6 (2016): 1048–61. http://dx.doi.org/10.1109/tmm.2016.2542585.

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Gong, Vincent X., Winnie Daamen, Alessandro Bozzon, and Serge P. Hoogendoorn. "Estimate Sentiment of Crowds from Social Media during City Events." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (2019): 836–50. http://dx.doi.org/10.1177/0361198119846461.

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City events are being organized more frequently, and with larger crowds, in urban areas. There is an increased need for novel methods and tools that can provide information on the sentiments of crowds as an input for crowd management. Previous work has explored sentiment analysis and a large number of methods have been proposed relating to various contexts. None of them, however, aimed at deriving the sentiments of crowds using social media in city events, and no existing event-based dataset is available for such studies. This paper investigates how social media can be used to estimate the sen

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Cao, Houwei, David G. Cooper, Michael K. Keutmann, Ruben C. Gur, Ani Nenkova, and Ragini Verma. "CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset." IEEE Transactions on Affective Computing 5, no. 4 (2014): 377–90. http://dx.doi.org/10.1109/taffc.2014.2336244.

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Masud, Mehedi, Parminder Singh, Gurjot Singh Gaba, et al. "CROWD: Crow Search and Deep Learning based Feature Extractor for Classification of Parkinson’s Disease." ACM Transactions on Internet Technology 21, no. 3 (2021): 1–18. http://dx.doi.org/10.1145/3418500.

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Edge Artificial Intelligence (AI) is the latest trend for next-generation computing for data analytics, particularly in predictive edge analytics for high-risk diseases like Parkinson’s Disease (PD). Deep learning learning techniques facilitate edge AI applications for enhanced, real-time handling of data. Dopamine is the cause of Parkinson’s that happens due to the interference of brain cells that produce the substance to regulate the communication of brain cells. The brain cells responsible for generating the dopamine perform adaptation, control, and movement with fluency. Parkinson’s motor

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Xiang, Jun, and Na Liu. "Crowd Density Estimation Method Using Deep Learning for Passenger Flow Detection System in Exhibition Center." Scientific Programming 2022 (February 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/1990951.

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Aiming at the problems of crowd distribution, scale feature, and crowd feature extraction difficulties in exhibition centers, this paper proposes a crowd density estimation method using deep learning for passenger flow detection systems in exhibition centers. Firstly, based on the pixel difference symbol feature, the difference amplitude feature and gray feature of the central pixel are extracted to form the CLBP feature to obtain more crowd group description information. Secondly, use the LR activation function to add nonlinear factors to the convolution neural network (CNN) and use dense blo

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Miao, Yunqi, Zijia Lin, Guiguang Ding, and Jungong Han. "Shallow Feature Based Dense Attention Network for Crowd Counting." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11765–72. http://dx.doi.org/10.1609/aaai.v34i07.6848.

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While the performance of crowd counting via deep learning has been improved dramatically in the recent years, it remains an ingrained problem due to cluttered backgrounds and varying scales of people within an image. In this paper, we propose a Shallow feature based Dense Attention Network (SDANet) for crowd counting from still images, which diminishes the impact of backgrounds via involving a shallow feature based attention model, and meanwhile, captures multi-scale information via densely connecting hierarchical image features. Specifically, inspired by the observation that backgrounds and h

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Bhuiyan, Md Roman, Junaidi Abdullah, Noramiza Hashim, et al. "Hajj pilgrimage video analytics using CNN." Bulletin of Electrical Engineering and Informatics 10, no. 5 (2021): 2598–606. http://dx.doi.org/10.11591/eei.v10i5.2361.

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This paper advances video analytics with a focus on crowd analysis for Hajj and Umrah pilgrimages. In recent years, there has been an increased interest in the advancement of video analytics and visible surveillance to improve the safety and security of pilgrims during their stay in Makkah. It is mainly because Hajj is an entirely special event that involve hundreds of thousands of people being clustered in a small area. This paper proposed a convolutional neural network (CNN) system for performing multitude analysis, in particular for crowd counting. In addition, it also proposes a new algori

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Larson, Martha, Mohammad Soleymani, Maria Eskevich, Pavel Serdyukov, Roeland Ordelman, and Gareth Jones. "The Community and the Crowd: Multimedia Benchmark Dataset Development." IEEE MultiMedia 19, no. 3 (2012): 15–23. http://dx.doi.org/10.1109/mmul.2012.27.

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Tahira, Memoona, Sobas Mehboob, Anis U. Rahman, and Omar Arif. "CrowdFix: An Eyetracking Dataset of Real Life Crowd Videos." IEEE Access 7 (2019): 179002–9. http://dx.doi.org/10.1109/access.2019.2956840.

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Zhang, Jun, Jiaze Liu, and Zhizhong Wang. "Convolutional Neural Network for Crowd Counting on Metro Platforms." Symmetry 13, no. 4 (2021): 703. http://dx.doi.org/10.3390/sym13040703.

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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 fea

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Mazzeo, Pier Luigi, Riccardo Contino, Paolo Spagnolo, et al. "MH-MetroNet—A Multi-Head CNN for Passenger-Crowd Attendance Estimation." Journal of Imaging 6, no. 7 (2020): 62. http://dx.doi.org/10.3390/jimaging6070062.

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Knowing an accurate passengers attendance estimation on each metro car contributes to the safely coordination and sorting the crowd-passenger in each metro station. In this work we propose a multi-head Convolutional Neural Network (CNN) architecture trained to infer an estimation of passenger attendance in a metro car. The proposed network architecture consists of two main parts: a convolutional backbone, which extracts features over the whole input image, and a multi-head layers able to estimate a density map, needed to predict the number of people within the crowd image. The network performa

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Ikeda, Kazushi, and Keiichiro Hoashi. "Utilizing Crowdsourced Asynchronous Chat for Efficient Collection of Dialogue Dataset." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 6 (June 15, 2018): 60–69. http://dx.doi.org/10.1609/hcomp.v6i1.13321.

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In this paper, we design a crowd-powered system to efficiently collect data for training dialogue systems. Conventional systems assign dialogue roles to a pair of crowd workers, and record their interaction on an online chat. In this framework, the pair is required to work simultaneously, and one worker must wait for the other when he/she is writing a message, which decreases work efficiency. Our proposed system allows multiple workers to create dialogues in an asynchronous manner, which relieves workers from time restrictions. We have conducted an experiment using our system on a crowdsourcin

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Ferryman, James, and Anna-Louise Ellis. "Performance evaluation of crowd image analysis using the PETS2009 dataset." Pattern Recognition Letters 44 (July 2014): 3–15. http://dx.doi.org/10.1016/j.patrec.2014.01.005.

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Guo, Chunsheng, Hanwen Lin, Zhen He, Xiaohu Shu, and Xuguang Zhang. "Crowd Abnormal Event Detection Based on Sparse Coding." International Journal of Humanoid Robotics 16, no. 04 (2019): 1941005. http://dx.doi.org/10.1142/s0219843619410056.

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Crowd feature perception is an essential step for us to understand the crowd behavior. However, as the individuals present not only the sociality but also the randomness, there remain great challenges to extract the sociality of the individual directly. In this paper, we propose a crowd feature perception algorithm based on a sparse linear model (SLM). It builds the statistical characterization of the sociality by assuming a priori distribution of the SLM. First, we calculate the optical flow to extract the motion information of the crowd. Second, we input the video motion features to the spar

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Coviello, Luca, Marco Cristoforetti, Giuseppe Jurman, and Cesare Furlanello. "GBCNet: In-Field Grape Berries Counting for Yield Estimation by Dilated CNNs." Applied Sciences 10, no. 14 (2020): 4870. http://dx.doi.org/10.3390/app10144870.

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We introduce here the Grape Berries Counting Net (GBCNet), a tool for accurate fruit yield estimation from smartphone cameras, by adapting Deep Learning algorithms originally developed for crowd counting. We test GBCNet using cross-validation procedure on two original datasets CR1 and CR2 of grape pictures taken in-field before veraison. A total of 35,668 berries have been manually annotated for the task. GBCNet achieves good performances on both the seven grape varieties dataset CR1, although with a different accuracy level depending on the variety, and on the single variety dataset CR2: in p

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Bilal, Muhammad, Mohsen Marjani, Ibrahim Abaker Targio Hashem, Abdullah Gani, Misbah Liaqat, and Kwangman Ko. "Profiling and Predicting the Cumulative Helpfulness (Quality) of Crowd-Sourced Reviews." Information 10, no. 10 (2019): 295. http://dx.doi.org/10.3390/info10100295.

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With easy access to the Internet and the popularity of online review platforms, the volume of crowd-sourced reviews is continuously rising. Many studies have acknowledged the importance of reviews in making purchase decisions. The consumer’s feedback plays a vital role in the success or failure of a business. The number of studies on predicting helpfulness and ranking reviews is increasing due to the increasing importance of reviews. However, previous studies have mainly focused on predicting helpfulness of “reviews” and “reviewer”. This study aimed to profile cumulative helpfulness received b

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Luo, Hongling, Jun Sang, Weiqun Wu, et al. "A High-Density Crowd Counting Method Based on Convolutional Feature Fusion." Applied Sciences 8, no. 12 (2018): 2367. http://dx.doi.org/10.3390/app8122367.

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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

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Shati, Narjis Mezaal. "Anomalous Behavior Detection Using the Geometrical Complex Moments in Crowd Scenes of Smart Surveillance Systems." Al-Mustansiriyah Journal of Science 28, no. 3 (2018): 174. http://dx.doi.org/10.23851/mjs.v28i3.35.

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In this research work a data stream clustering method done by extracting regions of interest from the frames of video clips (UCSD pedestrian dataset (ped1 and ped2 datasets) video clips, and VIRAT VIDEO dataset video clips). In extraction process the HARRIS or FAST detector applied on the frames of video clips to extract list of pairs of interest points. From these pairs a list of features such as: distance, direction, x-coordinate, y-coordinate obtained to use as an input to the clustering method based on seed based region growing technique. From these clusters a regions of interest extracted

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Zhang, Jun, Gaoyi Zhu, and Zhizhong Wang. "Multi-Column Atrous Convolutional Neural Network for Counting Metro Passengers." Symmetry 12, no. 4 (2020): 682. http://dx.doi.org/10.3390/sym12040682.

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We propose a symmetric method of accurately estimating the number of metro passengers from an individual image. To this end, we developed a network for metro-passenger counting called MPCNet, which provides a data-driven and deep learning method of understanding highly congested scenes and accurately estimating crowds, as well as presenting high-quality density maps. The proposed MPCNet is composed of two major components: A deep convolutional neural network (CNN) as the front end, for deep feature extraction; and a multi-column atrous CNN as the back-end, with atrous spatial pyramid pooling (

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Lalit, Ruchika, and Ravindra Kumar Purwar. "Crowd Abnormality Detection Using Optical Flow and GLCM-Based Texture Features." Journal of Information Technology Research 15, no. 1 (2022): 1–15. http://dx.doi.org/10.4018/jitr.2022010110.

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Detection of abnormal crowd behavior is one of the important tasks in real-time video surveillance systems for public safety in public places such as subway, shopping malls, sport complexes and various other public gatherings. Due to high density crowded scenes, the detection of crowd behavior becomes a tedious task. Hence, crowd behavior analysis becomes a hot topic of research and requires an approach with higher rate of detection. In this work, the focus is on the crowd management and present an end-to-end model for crowd behavior analysis. A feature extraction-based model using contrast, e

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Gretz, Shai, Roni Friedman, Edo Cohen-Karlik, et al. "A Large-Scale Dataset for Argument Quality Ranking: Construction and Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7805–13. http://dx.doi.org/10.1609/aaai.v34i05.6285.

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Identifying the quality of free-text arguments has become an important task in the rapidly expanding field of computational argumentation. In this work, we explore the challenging task of argument quality ranking. To this end, we created a corpus of 30,497 arguments carefully annotated for point-wise quality, released as part of this work. To the best of our knowledge, this is the largest dataset annotated for point-wise argument quality, larger by a factor of five than previously released datasets. Moreover, we address the core issue of inducing a labeled score from crowd annotations by perfo

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Setti, Francesco, Davide Conigliaro, Paolo Rota, et al. "The S-Hock dataset: A new benchmark for spectator crowd analysis." Computer Vision and Image Understanding 159 (June 2017): 47–58. http://dx.doi.org/10.1016/j.cviu.2017.01.003.

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Abir, Intiaz, Hasan Firdaus Mohd Zaki, and Azhar Mohd Ibrahim. "EVALUATION OF SIMULTANEOUS IDENTITY, AGE AND GENDER RECOGNITION FOR CROWD FACE MONITORING." ASEAN Engineering Journal 13, no. 1 (2023): 11–20. http://dx.doi.org/10.11113/aej.v13.17612.

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Nowadays, facial recognition combined with age estimation and gender prediction has been deeply involved with the factors associated with crowd monitoring. This is considered to be a major and complex job for humans. This paper proposes a unified facial recognition system based on already available deep learning and machine learning models (i.e., FaceNet, ResNet, Support Vector Machine, AgeNet and GenderNet) that automatically and simultaneously performs person identification, age estimation and gender prediction. Then the system is evaluated on a newly proposed multi-face, realistic and chall

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Kölle, M., V. Walter, S. Schmohl, and U. Soergel. "HYBRID ACQUISITION OF HIGH QUALITY TRAINING DATA FOR SEMANTIC SEGMENTATION OF 3D POINT CLOUDS USING CROWD-BASED ACTIVE LEARNING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 501–8. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-501-2020.

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Abstract. Automated semantic interpretation of 3D point clouds is crucial for many tasks in the domain of geospatial data analysis. For this purpose, labeled training data is required, which has often to be provided manually by experts. One approach to minimize effort in terms of costs of human interaction is Active Learning (AL). The aim is to process only the subset of an unlabeled dataset that is particularly helpful with respect to class separation. Here a machine identifies informative instances which are then labeled by humans, thereby increasing the performance of the machine. In order

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Valeri, Beatrice, Shady Elbassuoni, and Sihem Amer-Yahia. "Acquiring Reliable Ratings from the Crowd." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 3 (September 23, 2015): 40–41. http://dx.doi.org/10.1609/hcomp.v3i1.13261.

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We address the problem of acquiring reliable ratings of items such as restaurants or movies from the crowd. We propose a crowdsourcing platform that takes into consideration the workers’ skills with respect to the items being rated and assigns workers the best items to rate. Our platform focuses on acquiring ratings from skilled workers and for items that only have a few ratings. We evaluate the effectiveness of our system using a real-world dataset about restaurants.

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Ghadi, Yazeed Yasin, Israr Akhter, Hanan Aljuaid, et al. "Extrinsic Behavior Prediction of Pedestrians via Maximum Entropy Markov Model and Graph-Based Features Mining." Applied Sciences 12, no. 12 (2022): 5985. http://dx.doi.org/10.3390/app12125985.

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With the change of technology and innovation of the current era, retrieving data and data processing becomes a more challenging task for researchers. In particular, several types of sensors and cameras are used to collect multimedia data from various resources and domains, which have been used in different domains and platforms to analyze things such as educational and communicational setups, emergency services, and surveillance systems. In this paper, we propose a robust method to predict human behavior from indoor and outdoor crowd environments. While taking the crowd-based data as input, so

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Ros-Candeira, Andrea, Ricardo Moreno-Llorca, Domingo Alcaraz-Segura, Francisco Javier Bonet-García, and Ana Sofia Vaz. "Social media photo content for Sierra Nevada: a dataset to support the assessment of cultural ecosystem services in protected areas." Nature Conservation 38 (March 13, 2020): 1–12. http://dx.doi.org/10.3897/natureconservation.38.38325.

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This dataset provides crowd-sourced and georeferenced information useful for the assessment of cultural ecosystem services in the Sierra Nevada Biosphere Reserve (southern Spain). Data were collected within the European project ECOPOTENTIAL focused on Earth observations of ecosystem services. The dataset comprises 778 records expressing the results of the content analysis of social media photos published in Flickr. Our dataset is illustrated in this data paper with density maps for different types of information.

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Ros-Candeira, Andrea, Ricardo Moreno-Llorca, Domingo Alcaraz-Segura, Francisco Javier Bonet-García, and Ana Sofia Vaz. "Social media photo content for Sierra Nevada: a dataset to support the assessment of cultural ecosystem services in protected areas." Nature Conservation 38 (March 13, 2020): 1–12. http://dx.doi.org/10.3897/neobiota.38.38325.

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This dataset provides crowd-sourced and georeferenced information useful for the assessment of cultural ecosystem services in the Sierra Nevada Biosphere Reserve (southern Spain). Data were collected within the European project ECOPOTENTIAL focused on Earth observations of ecosystem services. The dataset comprises 778 records expressing the results of the content analysis of social media photos published in Flickr. Our dataset is illustrated in this data paper with density maps for different types of information.

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Burtsev, Mikhail, and Varvara Logacheva. "Conversational Intelligence Challenge: Accelerating Research with Crowd Science and Open Source." AI Magazine 41, no. 3 (2020): 18–27. http://dx.doi.org/10.1609/aimag.v41i3.5324.

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Development of conversational systems is one of the most challenging tasks in natural language processing, and it is especially hard in the case of open-domain dialogue. The main factors that hinder progress in this area are lack of training data and difficulty of automatic evaluation. Thus, to reliably evaluate the quality of such models, one needs to resort to time-consuming and expensive human evaluation. We tackle these problems by organizing the Conversational Intelligence Challenge (ConvAI) — open competition of dialogue systems. Our goals are threefold: to work out a good design for hum

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N, Sandeep, Ragul N.S, Nikil Dhas P, and Vaishnavi V. "Congestion Control early warning system using Deep Learning." International Journal of Computer Communication and Informatics 3, no. 2 (2021): 35–50. http://dx.doi.org/10.34256/ijcci2124.

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A new approach is proposed to analyze the live crowd and to provide an alert at the time of congestion, over-crowding and sudden gathering of pedestrians in a particular region. This paper proposes a completely software-oriented approach using MATLAB where it uses object detection and object tracking using Faster R- CNN (Region Based Convolutional Neural Network) algorithm where inception model of Google is used as CNN model which is pre-trained. This proposed method gives significant result on proposed dataset and the crowd congestion using Faster R-CNN approach which gives an accuracy of 93.

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Petrén Bach Hansen, Victor, and Anders Søgaard. "What Do You Mean ‘Why?’: Resolving Sluices in Conversations." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7887–94. http://dx.doi.org/10.1609/aaai.v34i05.6295.

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In conversation, we often ask one-word questions such as ‘Why?’ or ‘Who?’. Such questions are typically easy for humans to answer, but can be hard for computers, because their resolution requires retrieving both the right semantic frames and the right arguments from context. This paper introduces the novel ellipsis resolution task of resolving such one-word questions, referred to as sluices in linguistics. We present a crowd-sourced dataset containing annotations of sluices from over 4,000 dialogues collected from conversational QA datasets, as well as a series of strong baseline architectures

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Zhu, Rui, Kangning Yin, Hang Xiong, Hailian Tang, and Guangqiang Yin. "Masked Face Detection Algorithm in the Dense Crowd Based on Federated Learning." Wireless Communications and Mobile Computing 2021 (October 4, 2021): 1–8. http://dx.doi.org/10.1155/2021/8586016.

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Wearing masks is an effective and simple method to prevent the spread of the COVID-19 pandemic in public places, such as train stations, classrooms, and streets. It is of positive significance to urge people to wear masks with computer vision technology. However, the existing detection methods are mainly for simple scenes, and facial missing detection is prone to occur in dense crowds with different scales and occlusions. Moreover, the data obtained by surveillance cameras in public places are difficult to be collected for centralized training, due to the privacy of individuals. In order to so

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Stylianou, Abby, Hong Xuan, Maya Shende, Jonathan Brandt, Richard Souvenir, and Robert Pless. "Hotels-50K: A Global Hotel Recognition Dataset." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 726–33. http://dx.doi.org/10.1609/aaai.v33i01.3301726.

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Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms. To support efforts towards this hotel recognition task, we have curated a dataset of over 1 milli

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Patterson, Genevieve, Grant Van Horn, Serge Belongie, Pietro Perona, and James Hays. "Tropel: Crowdsourcing Detectors with Minimal Training." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 3 (September 23, 2015): 150–59. http://dx.doi.org/10.1609/hcomp.v3i1.13224.

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This paper introduces the Tropel system which enables non-technical users to create arbitrary visual detectors without first annotating a training set. Our primary contribution is a crowd active learning pipeline that is seeded with only a single positive example and an unlabeled set of training images. We examine the crowd's ability to train visual detectors given severely limited training themselves. This paper presents a series of experiments that reveal the relationship between worker training, worker consensus and the average precision of detectors trained by crowd-in-the-loop active lear

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Mayo, Hugo, Alastair Shipman, Daniele Giunchi, Riccardo Bovo, Anthony Steed, and Thomas Heinis. "VR Toolkit for Identifying Group Characteristics." Collective Dynamics 6 (February 3, 2022): 1. http://dx.doi.org/10.17815/cd.2021.119.

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Visualising crowds is a key pedestrian dynamics topic, with significant research efforts aiming to improve the current state-of-the-art. Sophisticated visualisation methods are a standard for modern commercial models, and can improve crowd management techniques and sociological theory development. These models often define standard metrics, including density and speed. However, modern visualisation techniques typically use desktop screens. This can limit the capability of a user to investigate and identify key features, especially in real time scenarios such as control centres. Virtual reality

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Csönde, Gergely, Yoshihide Sekimoto, and Takehiro Kashiyama. "Crowd Counting with Semantic Scene Segmentation in Helicopter Footage." Sensors 20, no. 17 (2020): 4855. http://dx.doi.org/10.3390/s20174855.

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Continually improving crowd counting neural networks have been developed in recent years. The accuracy of these networks has reached such high levels that further improvement is becoming very difficult. However, this high accuracy lacks deeper semantic information, such as social roles (e.g., student, company worker, or police officer) or location-based roles (e.g., pedestrian, tenant, or construction worker). Some of these can be learned from the same set of features as the human nature of an entity, whereas others require wider contextual information from the human surroundings. The primary

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Hameed, Mazhar, Fengbao Yang, Muhammad Imran Ghafoor, et al. "IOTA-Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit-Boosted Machine Learning Algorithms." Wireless Communications and Mobile Computing 2022 (April 23, 2022): 1–15. http://dx.doi.org/10.1155/2022/6274114.

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In the Internet of Things (IoT) era, the mobile crowd sensing system (MCS) has become increasingly important. The Internet of Things Auto (IOTA) has evolved rapidly in practically every technology field over the last decade. IOTA-based mobile crowd sensing technology is being developed in this study using machine learning to detect and prevent mobile users from engaging in fake sensing activities. It has been determined through testing and evaluation that our method is effective for both quality estimation and incentive allocation. Using the IOTA Bottleneck dataset, multiple performance metric

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Abdullah, Faisal, Yazeed Yasin Ghadi, Munkhjargal Gochoo, Ahmad Jalal, and Kibum Kim. "Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier." Entropy 23, no. 5 (2021): 628. http://dx.doi.org/10.3390/e23050628.

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To prevent disasters and to control and supervise crowds, automated video surveillance has become indispensable. In today’s complex and crowded environments, manual surveillance and monitoring systems are inefficient, labor intensive, and unwieldy. Automated video surveillance systems offer promising solutions, but challenges remain. One of the major challenges is the extraction of true foregrounds of pixels representing humans only. Furthermore, to accurately understand and interpret crowd behavior, human crowd behavior (HCB) systems require robust feature extraction methods, along with power

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He, Eric, Fan Bai, Curtis Hay, Jinzhu Chen, and Vijayakumar Bhagavatula. "A Map Inference Approach Using Signal Processing from Crowd-sourced GPS Data." ACM Transactions on Spatial Algorithms and Systems 7, no. 2 (2021): 1–23. http://dx.doi.org/10.1145/3431785.

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The amount of GPS data that can be collected is increasing tremendously, thanks to the increased popularity of Global Position System (GPS) devices (e.g., smartphones). This article aims to develop novel methods of converting crowd-sourced GPS traces into road topology maps. We explore map inference using a three-stage approach, which incorporates a novel Multi-source Variable Rate (MSVR) signal reconstruction mechanism. Unlike conventional map inference methods based on map graph theory, our approach, to the best of our knowledge, is the first use of estimation theory for map inference. In pa

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Nie, Pei, Cien Fan, Lian Zou, Liqiong Chen, and Xiaopeng Li. "Crowd Counting Guided by Attention Network." Information 11, no. 12 (2020): 567. http://dx.doi.org/10.3390/info11120567.

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Crowd Crowd counting is not simply a matter of counting the numbers of people, but also requires that one obtains people’s spatial distribution in a picture. It is still a challenging task for crowded scenes, occlusion, and scale variation. This paper proposes a global and local attention network (GLANet) for efficient crowd counting, which applies an attention mechanism to enhance the features. Firstly, the feature extractor module (FEM) uses the pertained VGG-16 to parse out a simple feature map. Secondly, the global and local attention module (GLAM) effectively captures the local and global

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Courty, Nicolas, Pierre Allain, Clement Creusot, and Thomas Corpetti. "Using the Agoraset dataset: Assessing for the quality of crowd video analysis methods." Pattern Recognition Letters 44 (July 2014): 161–70. http://dx.doi.org/10.1016/j.patrec.2014.01.004.

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