Extremely Overlapping Vehicle Counting

Guerrero-Gómez-Olmedo, Ricardo; Torre-Jiménez, Beatriz; López-Sastre, Roberto J.; Maldonado-Bascón, Saturnino; Oñoro-Rubio, Daniel

doi:10.1007/978-3-319-19390-8_48

Cited by 203 publications

(161 citation statements)

References 12 publications

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“…From left to right, for each visualization, the original image, the groundtruth count map, and the inferred count map, respectively. From top to down, left to right, the visualization of ShanghaiTech Part_A, Part_B [53], UCF_CC_50 [14], UCF-QNRF [15], TRANCOS [13], and MTC [25], respectively. The original image is zero-padded to be divisible by 64.…”

Section: Resultsmentioning

confidence: 99%

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From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

Xiong

Liu

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

164

107

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Visual counting, a task that aims to estimate the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in [0, +∞) in theory. However, collected data and labeled instances are limited in reality, which means that only a small closed set is observed. Existing methods typically model this task in a regression manner, while they are prone to suffer from an unseen scene with counts out of the scope of the closed set. In fact, counting has an interesting and exclusive property-spatially decomposable. A dense region can always be divided until sub-region counts are within the previously observed closed set. We therefore introduce the idea of spatial divide-and-conquer (S-DC) that transforms open-set counting into a closed-set problem. This idea is implemented by a novel Supervised Spatial Divide-and-Conquer Network (SS-DCNet). Thus, SS-DCNet can only learn from a closed set but generalize well to open-set scenarios via S-DC. SS-DCNet is also efficient. To avoid repeatedly computing sub-region convolutional features, S-DC is executed on the feature map instead of on the input image. We provide theoretical analyses as well as a controlled experiment on toy data, demonstrating why closed-set modeling makes sense. Extensive experiments show that SS-DCNet achieves the state-of-the-art performance on three crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QNRF), a vehicle counting dataset (TRANCOS) and a plant counting dataset (MTC), with a 7.7% relative improvement on the UCF-QNRF, 33.1% on the TRANCOS, and 26.4% on the MTC. SS-DCNet also reports the state-of-the-art cross-domain performance on crowd counting datasets. Particularly in the task from UCF-QNRF to ShanghaiTech Part_A, SS-DCNet even beats most existing models trained directly on the target domain. Code and models have been made available at: https://tinyurl.com/SS-DCNet.

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Section: Resultsmentioning

confidence: 99%

“…The best performance is in boldface. Table 9 Comparison with state-of-the-art approaches on the test set of TRANCOS [13] dataset. The best performance is in boldface.…”

Section: The Shanghaitech Datasetmentioning

confidence: 99%

From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

Xiong

Liu

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

164

107

View full text Add to dashboard Cite

show abstract

“…Our observation is that despite the improved global counting accuracy, significant local counting errors exist when diving into the predicted density map. This phenomenon has been reported in [6,23], however, it has not been sufficiently investigated and explicitly addressed.…”

Section: Introductionmentioning

confidence: 94%

“…TRANCOS We also report our results on another dataset for car counting to validate the effectiveness of the proposed method. TRANCOS [6] is a publicly available dataset which contains 1244 images of different traffic scenes obtained Fig. 4.…”

Section: Comparison With the State-of-the-artsmentioning

confidence: 99%

“…An ROI map is also provided for each image. We strictly follow the experimental setup proposed in [6] for training and testing, where there are 403, 420 and 421 images are settled for train, validation and test, respectively. Table 3 reports the comparison performance on this dataset with four stateof-the-art approaches: density MESA [11], regression forest [4], Hydra CNN [16] and MCNN [28].…”

Section: Comparison With the State-of-the-artsmentioning

confidence: 99%

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Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks

Zhao

Zhang

et al. 2019

Computer Vision – ACCV 2018

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High-density object counting in surveillance scenes is challenging mainly due to the drastic variation of object scales. The prevalence of deep learning has largely boosted the object counting accuracy on several benchmark datasets. However, does the global counts really count? Armed with this question we dive into the predicted density map whose summation over the whole regions reports the global counts for more in-depth analysis. We observe that the object density map generated by most existing methods usually lacks of local consistency, i.e., counting errors in local regions exist unexpectedly even though the global count seems to well match with the ground-truth. Towards this problem, in this paper we propose a constrained multi-stage Convolutional Neural Networks (CNNs) to jointly pursue locally consistent density map from two aspects. Different from most existing methods that mainly rely on the multi-column architectures of plain CNNs, we exploit a stacking formulation of plain CNNs. Benefited from the internal multi-stage learning process, the feature map could be repeatedly refined, allowing the density map to approach the ground-truth density distribution. For further refinement of the density map, we also propose a grid loss function. With finer local-region-based supervisions, the underlying model is constrained to generate locally consistent density values to minimize the training errors considering both the global and local counts accuracy. Experiments on two widely-tested object counting benchmarks with overall significant results compared with state-of-the-art methods demonstrate the effectiveness of our approach.

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Visual crowd analysis: Open research problems

Khan,

Menouar,

Hamila

2023

AI Magazine

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Over the last decade, there has been a remarkable surge in interest in automated crowd monitoring within the computer vision community. Modern deep‐learning approaches have made it possible to develop fully automated vision‐based crowd‐monitoring applications. However, despite the magnitude of the issue at hand, the significant technological advancements, and the consistent interest of the research community, there are still numerous challenges that need to be overcome. In this article, we delve into six major areas of visual crowd analysis, emphasizing the key developments in each of these areas. We outline the crucial unresolved issues that must be tackled in future works, in order to ensure that the field of automated crowd monitoring continues to progress and thrive. Several surveys related to this topic have been conducted in the past. Nonetheless, this article thoroughly examines and presents a more intuitive categorization of works, while also depicting the latest breakthroughs within the field, incorporating more recent studies carried out within the last few years in a concise manner. By carefully choosing prominent works with significant contributions in terms of novelty or performance gains, this paper presents a more comprehensive exposition of advancements in the current state‐of‐the‐art.

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Extremely Overlapping Vehicle Counting

Cited by 203 publications

References 12 publications

From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks

Visual crowd analysis: Open research problems

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