FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

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

doi:10.1109/iccv.2017.396

Cited by 213 publications

(91 citation statements)

References 41 publications

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“…ing estimating the number of vehicles in traffic congestion [29,30,56,14,28], counting the cells and bacteria from microscopic images [20,42,44,45,8], and animal crowd estimations for ecological survey [27,1,18], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Loss for Crowd Count Estimation With Point Supervision

Wei

Hong

et al. 2019

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

475

344

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In crowd counting datasets, each person is annotated by a point, which is usually the center of the head. And the task is to estimate the total count in a crowd scene. Most of the state-of-the-art methods are based on density map estimation, which convert the sparse point annotations into a "ground truth" density map through a Gaussian kernel, and then use it as the learning target to train a density map estimator. However, such a "ground-truth" density map is imperfect due to occlusions, perspective effects, variations in object shapes, etc. On the contrary, we propose Bayesian loss, a novel loss function which constructs a density contribution probability model from the point annotations. Instead of constraining the value at every pixel in the density map, the proposed training loss adopts a more reliable supervision on the count expectation at each annotated point. Without bells and whistles, the loss function makes substantial improvements over the baseline loss on all tested datasets. Moreover, our proposed loss function equipped with a standard backbone network, without using any external detectors or multi-scale architectures, plays favourably against the state of the arts. Our method outperforms previous best approaches by a large margin on the latest and largest UCF-QNRF dataset. The source code is available at https://github.com/ZhihengCV/ Baysian-Crowd-Counting. arXiv:1908.03684v1 [cs.CV] 10 Aug 2019 pixel, our proposed training loss supervises on the count expectation at each annotated point, instead.Extensive experimental evaluations show that the proposed loss function substantially outperforms the baseline training loss on UCF-QNRF [16], ShanghaiTech [57], and UCF CC 50 [15] benchmark datasets. Moreover, our proposed loss function equipped with the standard VGG-19 network [39] as backbone, without using any external detectors or multi-scale architectures, achieves the state-ofthe-art performances on all the benchmark datasets, especially with a magnificent improvement on the UCF-QNRF dataset compared to other methods.

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Section: Introductionmentioning

confidence: 99%

Bayesian Loss for Crowd Count Estimation With Point Supervision

Wei

Hong

et al. 2019

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

475

344

View full text Add to dashboard Cite

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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“…The most relevant works to us are [32,39], which also incorporate ConvLSTM for spatial-temporal modeling. However, they are used for consecutive video frames representation and aims to estimate the crowd counting on a given surveillance image instead of forecasting crowd flow evolution based on mobility data.…”

Section: Related Workmentioning

confidence: 99%

Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining

Zhang

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

228

143

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Traffic flow prediction is crucial for urban traffic management and public safety. Its key challenges lie in how to adaptively integrate the various factors that affect the flow changes. In this paper, we propose a unified neural network module to address this problem, called Attentive Crowd Flow Machine (ACFM), which is able to infer the evolution of the crowd flow by learning dynamic representations of temporally-varying data with an attention mechanism. Specifically, the ACFM is composed of two progressive ConvL-STM units connected with a convolutional layer for spatial weight prediction. The first LSTM takes the sequential flow density representation as input and generates a hidden state at each time-step for attention map inference, while the second LSTM aims at learning the effective spatial-temporal feature expression from attentionally weighted crowd flow features. Based on the ACFM, we further build a deep architecture with the application to citywide crowd flow prediction, which naturally incorporates the sequential and periodic data as well as other external influences. Extensive experiments on two standard benchmarks (i.e., crowd flow in Beijing and New York City) show that the proposed method achieves significant improvements over the state-of-the-art methods.

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FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

Cited by 213 publications

References 41 publications

Bayesian Loss for Crowd Count Estimation With Point Supervision

Bayesian Loss for Crowd Count Estimation With Point Supervision

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

Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining

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