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Journal articles on the topic 'Crowded scenes'

Author: Grafiati

Published: 28 June 2021

Last updated: 11 February 2022

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1

Elbishlawi, Sherif, Mohamed H. Abdelpakey, Agwad Eltantawy, Mohamed S. Shehata, and Mostafa M. Mohamed. "Deep Learning-Based Crowd Scene Analysis Survey." Journal of Imaging 6, no. 9 (September 11, 2020): 95. http://dx.doi.org/10.3390/jimaging6090095.

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Recently, our world witnessed major events that attracted a lot of attention towards the importance of automatic crowd scene analysis. For example, the COVID-19 breakout and public events require an automatic system to manage, count, secure, and track a crowd that shares the same area. However, analyzing crowd scenes is very challenging due to heavy occlusion, complex behaviors, and posture changes. This paper surveys deep learning-based methods for analyzing crowded scenes. The reviewed methods are categorized as (1) crowd counting and (2) crowd actions recognition. Moreover, crowd scene datasets are surveyed. In additional to the above surveys, this paper proposes an evaluation metric for crowd scene analysis methods. This metric estimates the difference between calculated crowed count and actual count in crowd scene videos.

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2

Altamimi, A. B., and H. Ullah. "Panic Detection in Crowded Scenes." Engineering, Technology & Applied Science Research 10, no. 2 (April 4, 2020): 5412–18. http://dx.doi.org/10.48084/etasr.3347.

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A crowd is a gathering of a huge number of individuals in a confined area. Early identification and detection of unusual behaviors in terms of panic occurring in crowded scenes are very important. Panic detection comprises of formulating normal scene behaviors and detecting and identifying non-matching behaviors. However, panic detection and recognition is a very difficult problem, especially when considering diverse scenes. Many methods proposed to cope with these problems have limited robustness as the density of the crowd varies. In order to handle this challenge, this paper proposes the integration of different features into a unified model. Discriminant binary patterns and neighborhood information are used to model complex and unique motion patterns in order to characterize different levels of features for diverse types of crowd scenes, focusing in particular on the detection of panic and non-pedestrian entities. The proposed method was evaluated considering two benchmark datasets and outperformed five existing methods.

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3

Chaudhry, Huma, Mohd Shafry Mohd Rahim, Tanzila Saba, and Amjad Rehman. "Crowd region detection in outdoor scenes using color spaces." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 02 (March 20, 2018): 1850012. http://dx.doi.org/10.1142/s1793962318500125.

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In the last few decades, crowd detection has gained much interest from the research community to assist a variety of applications in surveillance systems. While human detection in partially crowded scenarios have achieved many reliable works, a highly dense crowd-like situation still is far from being solved. Densely crowded scenes offer patterns that could be used to tackle these challenges. This problem is challenging due to the crowd volume, occlusions, clutter and distortion. Crowd region classification is a precursor to several types of applications. In this paper, we propose a novel approach for crowd region detection in outdoor densely crowded scenarios based on color variation context and RGB channel dissimilarity. Experimental results are presented to demonstrate the effectiveness of the new color-based features for better crowd region detection.

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4

Duan, Genquan, Haizhou Ai, Junliang Xing, Song Cao, and Shihong Lao. "Scene Aware Detection and Block Assignment Tracking in crowded scenes." Image and Vision Computing 30, no. 4-5 (May 2012): 292–305. http://dx.doi.org/10.1016/j.imavis.2012.02.008.

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5

Gnouma, Mariem, Ridha Ejbali, and Mourad Zaied. "Abnormal events’ detection in crowded scenes." Multimedia Tools and Applications 77, no. 19 (February 26, 2018): 24843–64. http://dx.doi.org/10.1007/s11042-018-5701-6.

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6

Gafni, Niv, and Andrei Sharf. "3D Motion Completion in Crowded Scenes." Computer Graphics Forum 33, no. 5 (August 2014): 65–74. http://dx.doi.org/10.1111/cgf.12432.

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7

Rho, Seungmin, Wenny Rahayu, and Uyen Trang Nguyen. "Intelligent video surveillance in crowded scenes." Information Fusion 24 (July 2015): 1–2. http://dx.doi.org/10.1016/j.inffus.2014.11.002.

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8

Conte, Donatello, Pasquale Foggia, Gennaro Percannella, and Mario Vento. "Counting moving persons in crowded scenes." Machine Vision and Applications 24, no. 5 (March 3, 2013): 1029–42. http://dx.doi.org/10.1007/s00138-013-0491-3.

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9

Chi, Cheng, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, and Xudong Zou. "PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 10639–46. http://dx.doi.org/10.1609/aaai.v34i07.6690.

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Pedestrian detection in crowded scenes is a challenging problem, because occlusion happens frequently among different pedestrians. In this paper, we propose an effective and efficient detection network to hunt pedestrians in crowd scenes. The proposed method, namely PedHunter, introduces strong occlusion handling ability to existing region-based detection networks without bringing extra computations in the inference stage. Specifically, we design a mask-guided module to leverage the head information to enhance the feature representation learning of the backbone network. Moreover, we develop a strict classification criterion by improving the quality of positive samples during training to eliminate common false positives of pedestrian detection in crowded scenes. Besides, we present an occlusion-simulated data augmentation to enrich the pattern and quantity of occlusion samples to improve the occlusion robustness. As a consequent, we achieve state-of-the-art results on three pedestrian detection datasets including CityPersons, Caltech-USA and CrowdHuman. To facilitate further studies on the occluded pedestrian detection in surveillance scenes, we release a new pedestrian dataset, called SUR-PED, with a total of over 162k high-quality manually labeled instances in 10k images. The proposed dataset, source codes and trained models are available at https://github.com/ChiCheng123/PedHunter.

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10

Leach, Michael J. V., Ed P. Sparks, and Neil M. Robertson. "Contextual anomaly detection in crowded surveillance scenes." Pattern Recognition Letters 44 (July 2014): 71–79. http://dx.doi.org/10.1016/j.patrec.2013.11.018.

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11

Gunduz, Ayse Elvan, Cihan Ongun, Tugba Taskaya Temizel, and Alptekin Temizel. "Density aware anomaly detection in crowded scenes." IET Computer Vision 10, no. 5 (February 9, 2016): 376–83. http://dx.doi.org/10.1049/iet-cvi.2015.0345.

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12

Lim, M. K., C. S. Chan, D. Monekosso, and P. Remagnino. "Detection of salient regions in crowded scenes." Electronics Letters 50, no. 5 (February 2014): 363–65. http://dx.doi.org/10.1049/el.2013.3993.

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13

Weixin Li, Vijay Mahadevan, and Nuno Vasconcelos. "Anomaly Detection and Localization in Crowded Scenes." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 1 (January 2014): 18–32. http://dx.doi.org/10.1109/tpami.2013.111.

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14

Chen, Xiaofeng, Kristian Henrickson, and Yinhai Wang. "Kinect-Based Pedestrian Detection for Crowded Scenes." Computer-Aided Civil and Infrastructure Engineering 31, no. 3 (July 24, 2015): 229–40. http://dx.doi.org/10.1111/mice.12163.

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15

Xu, Ming, Xiaosheng Yu, Dongyue Chen, Chengdong Wu, and Yang Jiang. "An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders." Applied Sciences 9, no. 16 (August 14, 2019): 3337. http://dx.doi.org/10.3390/app9163337.

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Anomaly detection in crowded scenes is an important and challenging part of the intelligent video surveillance system. As the deep neural networks make success in feature representation, the features extracted by a deep neural network represent the appearance and motion patterns in different scenes more specifically, comparing with the hand-crafted features typically used in the traditional anomaly detection approaches. In this paper, we propose a new baseline framework of anomaly detection for complex surveillance scenes based on a variational auto-encoder with convolution kernels to learn feature representations. Firstly, the raw frames series are provided as input to our variational auto-encoder without any preprocessing to learn the appearance and motion features of the receptive fields. Then, multiple Gaussian models are used to predict the anomaly scores of the corresponding receptive fields. Our proposed two-stage anomaly detection system is evaluated on the video surveillance dataset for a large scene, UCSD pedestrian datasets, and yields competitive performance compared with state-of-the-art methods.

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16

JIANG, Jun, Di WU, Qizhi TENG, Xiaohai HE, and Mingliang GAO. "Measuring Collectiveness in Crowded Scenes via Link Prediction." IEICE Transactions on Information and Systems E98.D, no. 8 (2015): 1617–20. http://dx.doi.org/10.1587/transinf.2015edl8011.

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17

Shi, Yanjiao, Yunxiang Liu, Qing Zhang, Yugen Yi, and Wenju Li. "Saliency-based abnormal event detection in crowded scenes." Journal of Electronic Imaging 25, no. 6 (September 9, 2016): 061608. http://dx.doi.org/10.1117/1.jei.25.6.061608.

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18

Fu, Zufeng. "Exploiting context for people detection in crowded scenes." Journal of Electronic Imaging 27, no. 04 (July 30, 2018): 1. http://dx.doi.org/10.1117/1.jei.27.4.043028.

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19

Li, Ang, Zhenjiang Miao, Yigang Cen, and Yi Cen. "Anomaly detection using sparse reconstruction in crowded scenes." Multimedia Tools and Applications 76, no. 24 (December 29, 2016): 26249–71. http://dx.doi.org/10.1007/s11042-016-4115-6.

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20

Kelly, Philip, Noel E. O’Connor, and Alan F. Smeaton. "Robust pedestrian detection and tracking in crowded scenes." Image and Vision Computing 27, no. 10 (September 2009): 1445–58. http://dx.doi.org/10.1016/j.imavis.2008.04.006.

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21

Boyrazlı, Hatice Kübra, and Ahmet Çınar. "ANOMALY DETECTION WITH MACHINE LEARNING ALGORITHMS IN CROWDED SCENES IN UMN ANOMALY DATASET." e-Journal of New World Sciences Academy 16, no. 1 (January 30, 2021): 1–6. http://dx.doi.org/10.12739/nwsa.2021.16.1.2a0185.

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22

HASHEMZADEH, MAHDI, GANG PAN, YUEMING WANG, MIN YAO, and JIAN WU. "COMBINING VELOCITY AND LOCATION-SPECIFIC SPATIAL CLUES IN TRAJECTORIES FOR COUNTING CROWDED MOVING OBJECTS." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 02 (March 2013): 1354003. http://dx.doi.org/10.1142/s0218001413540037.

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Trajectory-clustering-based methods have shown a good performance in counting moving objects in densely crowded scenes. However, they still fall into trouble in complex scenes, such as with the close proximity of moving objects, freely moving parts of objects, and different object size in different locations of the scene. This paper proposes a new method combining velocity and location-specific spatial clues in trajectories to deal with these problems. We first extract the velocities of a trajectory over its life-time. To alleviate confusion around the boundary regions between close objects, extracted velocity information is utilized to eliminate unreal-world feature points on objects' boundaries. Then, a function is introduced to measure the similarity of the trajectories integrating both of the spatial and the velocity clues. This function is employed in the Mean-Shift clustering procedure to reduce the effect of freely moving parts of the objects. To address the problem of various object sizes in different regions of the scene, we suggest a technique to learn the location-specific size distribution of objects in different locations of a scene. The experimental results show that our proposed method achieves a good performance. Compared with other trajectory-clustering-based methods, it decreases the counting error rate by about 10%.

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23

Thida, Myo, How-Lung Eng, Dorothy N. Monekosso, and Paolo Remagnino. "Learning Video Manifolds for Content Analysis of Crowded Scenes." IPSJ Transactions on Computer Vision and Applications 4 (2012): 71–77. http://dx.doi.org/10.2197/ipsjtcva.4.71.

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24

Cong, Yang, Junsong Yuan, and Ji Liu. "Abnormal event detection in crowded scenes using sparse representation." Pattern Recognition 46, no. 7 (July 2013): 1851–64. http://dx.doi.org/10.1016/j.patcog.2012.11.021.

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25

Zhu, Xiaobin, Jing Liu, Jinqiao Wang, Changsheng Li, and Hanqing Lu. "Sparse representation for robust abnormality detection in crowded scenes." Pattern Recognition 47, no. 5 (May 2014): 1791–99. http://dx.doi.org/10.1016/j.patcog.2013.11.018.

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26

Chen, Tianyu, Chunping Hou, Zhipeng Wang, and Hua Chen. "Anomaly detection in crowded scenes using motion energy model." Multimedia Tools and Applications 77, no. 11 (July 14, 2017): 14137–52. http://dx.doi.org/10.1007/s11042-017-5020-3.

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27

Shao, Jie, Nan Dong, and Minglei Tong. "Multi-part sparse representation in random crowded scenes tracking." Pattern Recognition Letters 34, no. 7 (May 2013): 780–88. http://dx.doi.org/10.1016/j.patrec.2012.07.008.

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28

O’Gorman, Lawrence, Yafeng Yin, and Tin Kam Ho. "Motion feature filtering for event detection in crowded scenes." Pattern Recognition Letters 44 (July 2014): 80–87. http://dx.doi.org/10.1016/j.patrec.2013.08.020.

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29

Cho, Sang-Hyun, and Hang-Bong Kang. "Abnormal behavior detection using hybrid agents in crowded scenes." Pattern Recognition Letters 44 (July 2014): 64–70. http://dx.doi.org/10.1016/j.patrec.2013.11.017.

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30

Deng, Chunhua, Zhiguo Cao, Yang Xiao, Hao Lu, Ke Xian, and Yin Chen. "Exploiting Attribute Dependency for Attribute Assignment in Crowded Scenes." IEEE Signal Processing Letters 23, no. 10 (October 2016): 1325–29. http://dx.doi.org/10.1109/lsp.2016.2592689.

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31

Wang, Lu, and Nelson Hon Ching Yung. "Three-Dimensional Model-Based Human Detection in Crowded Scenes." IEEE Transactions on Intelligent Transportation Systems 13, no. 2 (June 2012): 691–703. http://dx.doi.org/10.1109/tits.2011.2179536.

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32

Wang, Tian, Meina Qiao, Zhiwei Lin, Ce Li, Hichem Snoussi, Zhe Liu, and Chang Choi. "Generative Neural Networks for Anomaly Detection in Crowded Scenes." IEEE Transactions on Information Forensics and Security 14, no. 5 (May 2019): 1390–99. http://dx.doi.org/10.1109/tifs.2018.2878538.

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33

Jiang, M., J. Xu, and Q. Zhao. "Where Do People Look at in Crowded Natural Scenes?" Journal of Vision 14, no. 10 (August 22, 2014): 1052. http://dx.doi.org/10.1167/14.10.1052.

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34

Sohoglu, Ediz, and Maria Chait. "Neural dynamics of change detection in crowded acoustic scenes." NeuroImage 126 (February 2016): 164–72. http://dx.doi.org/10.1016/j.neuroimage.2015.11.050.

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35

Shehab, Doaa, and Heyfa Ammar. "Statistical detection of a panic behavior in crowded scenes." Machine Vision and Applications 30, no. 5 (September 18, 2018): 919–31. http://dx.doi.org/10.1007/s00138-018-0974-3.

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36

Alyammahi, Sohailah, Harish Bhaskar, Dymitr Ruta, and Mohammed Al-Mualla. "People detection and articulated pose estimation framework for crowded scenes." Knowledge-Based Systems 131 (September 2017): 83–104. http://dx.doi.org/10.1016/j.knosys.2017.06.001.

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37

Yuan, Yuan, Yachuang Feng, and Xiaoqiang Lu. "Structured dictionary learning for abnormal event detection in crowded scenes." Pattern Recognition 73 (January 2018): 99–110. http://dx.doi.org/10.1016/j.patcog.2017.08.001.

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38

Chongjing, Wang, Zhao Xu, Zou Yi, and Liu Yuncai. "Analyzing motion patterns in crowded scenes via automatic tracklets clustering." China Communications 10, no. 4 (April 2013): 144–54. http://dx.doi.org/10.1109/cc.2013.6506940.

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39

Tian, Yonghong, Yaowei Wang, Zhipeng Hu, and Tiejun Huang. "Selective Eigenbackground for Background Modeling and Subtraction in Crowded Scenes." IEEE Transactions on Circuits and Systems for Video Technology 23, no. 11 (November 2013): 1849–64. http://dx.doi.org/10.1109/tcsvt.2013.2248239.

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40

Yuan, Yuan, Yachuang Feng, and Xiaoqiang Lu. "Statistical Hypothesis Detector for Abnormal Event Detection in Crowded Scenes." IEEE Transactions on Cybernetics 47, no. 11 (November 2017): 3597–608. http://dx.doi.org/10.1109/tcyb.2016.2572609.

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41

Zhang, Yang, Hao Sheng, Yubin Wu, Shuai Wang, Wei Ke, and Zhang Xiong. "Multiplex Labeling Graph for Near-Online Tracking in Crowded Scenes." IEEE Internet of Things Journal 7, no. 9 (September 2020): 7892–902. http://dx.doi.org/10.1109/jiot.2020.2996609.

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42

Amraee, Somaieh, Abbas Vafaei, Kamal Jamshidi, and Peyman Adibi. "Abnormal event detection in crowded scenes using one-class SVM." Signal, Image and Video Processing 12, no. 6 (March 8, 2018): 1115–23. http://dx.doi.org/10.1007/s11760-018-1267-z.

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43

Turkay, Cagatay, Emre Koc, and Selim Balcisoy. "An information theoretic approach to camera control for crowded scenes." Visual Computer 25, no. 5-7 (March 3, 2009): 451–59. http://dx.doi.org/10.1007/s00371-009-0337-1.

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44

Nam, Yunyoung. "Loitering detection using an associating pedestrian tracker in crowded scenes." Multimedia Tools and Applications 74, no. 9 (December 3, 2013): 2939–61. http://dx.doi.org/10.1007/s11042-013-1763-7.

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45

Fang, Zhijun, Fengchang Fei, Yuming Fang, Changhoon Lee, Naixue Xiong, Lei Shu, and Sheng Chen. "Abnormal event detection in crowded scenes based on deep learning." Multimedia Tools and Applications 75, no. 22 (February 13, 2016): 14617–39. http://dx.doi.org/10.1007/s11042-016-3316-3.

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46

Pai, Abhilash K., A. Kotegar Karunakar, and U. Raghavendra. "Scene-Independent Motion Pattern Segmentation in Crowded Video Scenes Using Spatio-Angular Density-Based Clustering." IEEE Access 8 (2020): 145984–94. http://dx.doi.org/10.1109/access.2020.3015375.

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47

Huang, Shaonian, Dongjun Huang, and Xinmin Zhou. "Learning Multimodal Deep Representations for Crowd Anomaly Event Detection." Mathematical Problems in Engineering 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/6323942.

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Anomaly event detection in crowd scenes is extremely important; however, the majority of existing studies merely use hand-crafted features to detect anomalies. In this study, a novel unsupervised deep learning framework is proposed to detect anomaly events in crowded scenes. Specifically, low-level visual features, energy features, and motion map features are simultaneously extracted based on spatiotemporal energy measurements. Three convolutional restricted Boltzmann machines are trained to model the mid-level feature representation of normal patterns. Then a multimodal fusion scheme is utilized to learn the deep representation of crowd patterns. Based on the learned deep representation, a one-class support vector machine model is used to detect anomaly events. The proposed method is evaluated using two available public datasets and compared with state-of-the-art methods. The experimental results show its competitive performance for anomaly event detection in video surveillance.

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48

Fischer, Jason, and David Whitney. "Object-level visual information gets through the bottleneck of crowding." Journal of Neurophysiology 106, no. 3 (September 2011): 1389–98. http://dx.doi.org/10.1152/jn.00904.2010.

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Natural visual scenes are cluttered. In such scenes, many objects in the periphery can be crowded, blocked from identification, simply because of the dense array of clutter. Outside of the fovea, crowding constitutes the fundamental limitation on object recognition and is thought to arise from the limited resolution of the neural mechanisms that select and bind visual features into coherent objects. Thus it is widely believed that in the visual processing stream, a crowded object is reduced to a collection of dismantled features with no surviving holistic properties. Here, we show that this is not so: an entire face can survive crowding and contribute its holistic attributes to the perceived average of the set, despite being blocked from recognition. Our results show that crowding does not dismantle high-level object representations to their component features.

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49

Vatsaraj, Meenal Suryakant, Rajan Vishnu Parab, and D. S. Bade. "ANOMALY DETECTION OF EVENTS IN CROWDED ENVIRONMENT AND STUDY OF VARIOUS BACKGROUND SUBTRACTION METHODS." International Journal of Students' Research in Technology & Management 5, no. 1 (May 6, 2017): 32. http://dx.doi.org/10.18510/ijsrtm.2017.517(1).

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Anomalous behavior detection and localization in videos of the crowded area that is specific from a dominant pattern are obtained. Appearance and motion information are taken into account to robustly identify different kinds of an anomaly considering a wide range of scenes. Our concept based on a histogram of oriented gradients and Markov random field easily captures varying dynamic of the crowded environment.Histogram of oriented gradients along with well-known Markov random field will effectively recognize and characterizes each frame of each scene. Anomaly detection using artificial neural network consist both appearance and motion features which extract within spatio temporal domain of moving pixels that ensures robustness to local noise and thus increases accuracy in detection of a local anomaly with low computational cost.To extract a region of interest we have to subtract background. Background subtraction is done by various methods like Weighted moving mean, Gaussian mixture model, Kernel density estimation.

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50

CHEN, Chen, Huaxin XIAO, Yu LIU, and Maojun ZHANG. "Dual-Task Integrated Network for Fast Pedestrian Detection in Crowded Scenes." IEICE Transactions on Information and Systems E103.D, no. 6 (June 1, 2020): 1371–79. http://dx.doi.org/10.1587/transinf.2019edp7285.

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