Crowd counting using adaptive segmentation in a congregation

Deep learning occupies an undisputed dominance in crowd counting. This paper proposes a novel convolutional neural network architecture called SegCrowdNet. Despite the complex background in crowd scenes, the proposed SegCrowdNet still adaptively highlights the human head region and suppresses the non‐head region by segmentation. With the guidance of an attention mechanism, the proposed SegCrowdNet pays more attention to the human head region and automatically encodes the highly refined density map. The crowd count can be obtained by integrating the density map. To adapt the variation of crowd counts, SegCrowdNet intelligently classifies the crowd count of each image into several groups. In addition, the multi‐scale features are learned and extracted in the proposed SegCrowdNet to overcome the scale variations of the crowd. To verify the effectiveness of this proposed method, extensive experiments are conducted on four challenging datasets. The results demonstrate that the proposed SegCrowdNet achieves excellent performance compared with the state‐of‐the‐art methods.

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“…The researches [19][20][21][22][23][24][25] about crowd counting are too rich to elaborate all of them. Next, we briefly review some of them.…”

Section: Related Workmentioning

confidence: 99%

“…The total number of positive samples represents the crowd count. [26][27][28] complete the crowd counting task by using the aforementioned method, and [22] leverages adaptive thresholds to binarize the image to detect the crowd. They can get a good result in the sparse crowd scene.…”

Section: Counting By Detectionmentioning

confidence: 99%

Crowd counting with segmentation attention convolutional neural network

Chen

Wang

2021

IET Image Processing

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“…Another technique for counting pilgrims in a crowded environment was proposed by Sajid et al, [72]. A slit window is used to mark the area to count people and adaptive thresholding is used to filter and count the relevant blobs to estimate the number of pilgrims.…”

Section: Computer Visionmentioning

confidence: 99%

Digital Revolution for Hajj Crowd Management: A Technology Survey

et al. 2020

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“…To monitor, control, and protect crowds, accurate information about numbers plays a vital role in operational and security efficiencies [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. The counting and tracking of many persons is a challenging problem [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ] due to occlusions, the constant displacement of people, different perspectives and behaviors, varying illumination levels, and because, as the crowd gets bigger, the allocation of pixels per person decreases.…”

Section: Introductionmentioning

confidence: 99%

Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier

Abdullah

Ghadi

Gochoo

et al. 2021

Entropy

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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 powerful and reliable decision-making classifiers. In this paper, we describe our approach to these issues by presenting a novel Particles Force Model for multi-person tracking, a vigorous fusion of global and local descriptors, along with a robust improved entropy classifier for detecting and interpreting crowd behavior. In the proposed model, necessary preprocessing steps are followed by the application of a first distance algorithm for the removal of background clutter; true-foreground elements are then extracted via a Particles Force Model. The detected human forms are then counted by labeling and performing cluster estimation, using a K-nearest neighbors search algorithm. After that, the location of all the human silhouettes is fixed and, using the Jaccard similarity index and normalized cross-correlation as a cost function, multi-person tracking is performed. For HCB detection, we introduced human crowd contour extraction as a global feature and a particles gradient motion (PGD) descriptor, along with geometrical and speeded up robust features (SURF) for local features. After features were extracted, we applied bat optimization for optimal features, which also works as a pre-classifier. Finally, we introduced a robust improved entropy classifier for decision making and automated crowd behavior detection in smart surveillance systems. We evaluated the performance of our proposed system on a publicly available benchmark PETS2009 and UMN dataset. Experimental results show that our system performed better compared to existing well-known state-of-the-art methods by achieving higher accuracy rates. The proposed system can be deployed to great benefit in numerous public places, such as airports, shopping malls, city centers, and train stations to control, supervise, and protect crowds.

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Crowd counting using adaptive segmentation in a congregation

Cited by 10 publications

References 6 publications

Crowd counting with segmentation attention convolutional neural network

Crowd counting with segmentation attention convolutional neural network

Digital Revolution for Hajj Crowd Management: A Technology Survey

Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier

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