Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method

Sindagi, Vishwanath A.; Yasarla, Rajeev; Patel, Vishal M.

doi:10.1109/iccv.2019.00131

Cited by 76 publications

(38 citation statements)

References 59 publications

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“…Traditional approaches can be further classified to detection-, regression-and density estimation-based approaches. While the CNN-based approaches count the number of people in an image using the advancements driven primarily by CNN network (Wang et al 2020;Sindagi et al 2019;Ma et al 2019;Sindagi and Patel 2019b;Jiang et al 2019;Shi et al 2019;Sindagi and Patel 2019a). The CNN based methods can be further classified based on network property and training approach.…”

Section: Sendmentioning

confidence: 99%

Counting people in the crowd using social media images for crowd management in city events

et al. 2021

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City events are getting popular and are attracting a large number of people. This increase needs for methods and tools to provide stakeholders with crowd size information for crowd management purposes. Previous works proposed a large number of methods to count the crowd using different data in various contexts, but no methods proposed using social media images in city events and no datasets exist to evaluate the effectiveness of these methods. In this study we investigate how social media images can be used to estimate the crowd size in city events. We construct a social media dataset, compare the effectiveness of face recognition, object recognition, and cascaded methods for crowd size estimation, and investigate the impact of image characteristics on the performance of selected methods. Results show that object recognition based methods, reach the highest accuracy in estimating the crowd size using social media images in city events. We also found that face recognition and object recognition methods are more suitable to estimate the crowd size for social media images which are taken in parallel view, with selfies covering people in full face and in which the persons in the background have the same distance to the camera. However, cascaded methods are more suitable for images taken from top view with gatherings distributed in gradient. The created social media dataset is essential for selecting image characteristics and evaluating the accuracy of people counting methods in an urban event context.

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Section: Sendmentioning

confidence: 99%

Counting people in the crowd using social media images for crowd management in city events

et al. 2021

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“…Starting from pre-deep learning era, regression based methods [17], [18], [19], [32], [33], [34], usually first 6segment foreground regions and extract various low-level features, and utilize a regression model, such as ridge regression [18], Gaussian process regression(GPR) [17] to estimate crowd count. In deep learning era, people formulate the crowd counting problem as a density map regression problem [35], [11], [36], [37], [38], [39], [21]. Zhang et al [28] proposed to utilize a patch based crowd counting method by CNN.…”

Section: ) Regression Based Crowd Countingmentioning

confidence: 99%

“…Our method follows density map regression methods. Previous density regression based works [21], [41], [12], [39] usually first extract image/patch features using a backbone network(e.g. VGG16 [40]), and then perform density regression.…”

Section: ) Regression Based Crowd Countingmentioning

confidence: 99%

Cascaded Multi-Task Learning of Head Segmentation and Density Regression for RGBD Crowd Counting

Zhou

2020

IEEE Access

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In this paper we propose a novel regression based RGBD crowd counting method. Compared with previous RGBD crowd counting methods which mainly exploit depth cue to facilitate person/head detection, our approach adopts density map regression and is more robust to severe occlusion under dense crowded scenarios. We develop a cascaded depth-aware counting network that jointly performs head segmentation and density map regression. Our network explicitly feeds depth map at each stage so that depth cues are sufficiently exploited. The multi-task strategy allows the network to explicitly attent to foreground regions of a crowd scene and improve density regression. To generate the ground truth of head segmentation and density map, we propose a head scale estimation method according to the basic geometric assumption and camera projection function. Experiments on two public RGBD crowd counting benchmarks, ShanghaiTechRGBD dataset and MICC dataset show that the proposed method achieves new state-of-theart on both datasets. Further, our method can be easily extended to RGB datasets and achieves comparable performances on WorldExpo'10 dataset and UCF-QNRF dataset.

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“…Certain scholars have attempted to tackle perspective distortions by using focused attention mechanisms [ 12 , 13 , 14 , 15 , 16 ]. Learning residual errors and correcting density estimations with these errors has also proven to be a viable approach [ 17 , 18 , 19 ].…”

Section: Related Workmentioning

confidence: 99%

Crowd Counting with Semantic Scene Segmentation in Helicopter Footage

Csönde

Sekimoto

Kashiyama

2020

Sensors

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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 end-goal of developing recognition software is to involve them in autonomous decision-making systems. Therefore, it must be foolproof, which is, it must have good semantic understanding of the input. In this study, we focus on counting pedestrians in helicopter footage and introduce a dataset created from helicopter videos for this purpose. We use semantic segmentation to extract the required additional contextual information from the surroundings of an entity. We demonstrate that it is possible to increase the pedestrian counting accuracy in this manner. Furthermore, we show that crowd counting and semantic segmentation can be simultaneously achieved, with comparable or even improved accuracy, by using the same crowd counting neural network for both tasks through hard parameter sharing. The presented method is generic and it can be applied to arbitrary crowd density estimation methods. A link to the dataset is available at the end of the paper.

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Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method

Cited by 76 publications

References 59 publications

Counting people in the crowd using social media images for crowd management in city events

Counting people in the crowd using social media images for crowd management in city events

Cascaded Multi-Task Learning of Head Segmentation and Density Regression for RGBD Crowd Counting

Crowd Counting with Semantic Scene Segmentation in Helicopter Footage

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