Understanding Traffic Density from Large-Scale Web Camera Data

Zhang, Shanghang; Wu, Guanhang; Costeira, João Paulo; Moura, J.M.F.

doi:10.1109/cvpr.2017.454

Cited by 118 publications

(73 citation statements)

References 30 publications

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“…In this paper, we focus on the task of estimating crowd count and high-quality density maps which has wide applications in video surveillance [15,41], traffic monitoring, public safety, urban planning [43], scene understanding and flow monitoring. Also, the methods developed for crowd counting can be extended to counting tasks in other fields such as cell microscopy [38,36,16,6], vehicle counting [23,49,48,11,34], environmental survey [8,43], etc. The task of crowd counting and density estimation has seen a significant progress in the recent years.…”

Section: Introductionmentioning

confidence: 99%

Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs

Sindagi

Patel

2017

2017 IEEE International Conference on Computer Vision (ICCV)

624

461

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We present a novel method called Contextual Pyramid CNN (CP-CNN) for generating high-quality crowd density and count estimation by explicitly incorporating global and local contextual information of crowd images. The proposed CP-CNN consists of four modules: Global Context Estimator (GCE), Local Context Estimator (LCE), Density Map Estimator (DME) and a Fusion-CNN (F-CNN). GCE is a VGG-16 based CNN that encodes global context and it is trained to classify input images into different density classes, whereas LCE is another CNN that encodes local context information and it is trained to perform patch-wise classification of input images into different density classes. DME is a multi-column architecture-based CNN that aims to generate high-dimensional feature maps from the input image which are fused with the contextual information estimated by GCE and LCE using F-CNN. To generate high resolution and high-quality density maps, F-CNN uses a set of convolutional and fractionally-strided convolutional layers and it is trained along with the DME in an end-to-end fashion using a combination of adversarial loss and pixellevel Euclidean loss. Extensive experiments on highly challenging datasets show that the proposed method achieves significant improvements over the state-of-the-art methods.Recent CNN-based methods using different multi-scale architectures [50,23,29] have achieved significant success in addressing some of the above issues, especially in the high-density complex crowded scenes. However, these methods tend to under-estimate or over-estimate count in the presence of high-density and low-density crowd images, respectively (as shown in Fig. 2). A potential solution is to use contextual information during the learning process. Several recent works for semantic segmentation [21], scene parsing [51] and visual saliency [52] have demonstrated that incorporating contextual information can provide significant improvements in the results. Motivated by their success, we believe that availability of global context shall aid the learning process and help us achieve better

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Section: Introductionmentioning

confidence: 99%

Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs

Sindagi

Patel

2017

2017 IEEE International Conference on Computer Vision (ICCV)

624

461

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show abstract

“…Computer vision-based crowd counting [8,17,26,27,36,44,48,56,68,69,74,77] has witnessed tremendous progress in the recent years. Algorithms developed for crowd counting have found a variety of applications such as video and traffic surveillance [15,21,38,59,64,71,72], agriculture monitoring (plant counting) [35], cell counting [22], scene understanding, urban planning and environmental survey [11,68].…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting

Sindagi

Patel

2019

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

182

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Crowd counting presents enormous challenges in the form of large variation in scales within images and across the dataset. These issues are further exacerbated in highly congested scenes. Approaches based on straightforward fusion of multi-scale features from a deep network seem to be obvious solutions to this problem. However, these fusion approaches do not yield significant improvements in the case of crowd counting in congested scenes. This is usually due to their limited abilities in effectively combining the multi-scale features for problems like crowd counting. To overcome this, we focus on how to efficiently leverage information present in different layers of the network. Specifically, we present a network that involves: (i) a multilevel bottom-top and top-bottom fusion (MBTTBF) method to combine information from shallower to deeper layers and vice versa at multiple levels, (ii) scale complementary feature extraction blocks (SCFB) involving cross-scale residual functions to explicitly enable flow of complementary features from adjacent conv layers along the fusion paths. Furthermore, in order to increase the effectiveness of the multi-scale fusion, we employ a principled way of generating scale-aware ground-truth density maps for training. Experiments conducted on three datasets that contain highly congested scenes (ShanghaiTech, UCF CROWD 50, and UCF-QNRF) demonstrate that the proposed method is able to outperform several recent methods in all the datasets.

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“…Across all the levels of GAME, our method achieves the best results compared to other approaches. There is another work [27] reporting their GAME∼0 result of 5.31 on this dataset. However, the other three metrics (GAME∼1, 2, 3) are unavailable for direct and effective comparison.…”

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

confidence: 98%

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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Understanding Traffic Density from Large-Scale Web Camera Data

Cited by 118 publications

References 30 publications

Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs

Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs

Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting

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

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