Convolutional Neural Network (CNN) based methods generally take crowd counting as a regression task by outputting crowd densities. They learn the mapping between image contents and crowd density distributions. Though having achieved promising results, these data-driven counting networks are prone to overestimate or underestimate people counts of regions with different density patterns, which degrades the whole count accuracy. To overcome this problem, we propose an approach to alleviate the counting performance differences in different regions. Specifically, our approach consists of two networks named Density Attention Network (DANet) and Attention Scaling Network (ASNet). DANet provides ASNet with attention masks related to regions of different density levels. ASNet first generates density maps and scaling factors and then multiplies them by attention masks to output separate attention-based density maps. These density maps are summed to give the final density map. The attention scaling factors help attenuate the estimation errors in different regions. Furthermore, we present a novel Adaptive Pyramid Loss (APLoss) to hierarchically calculate the estimation losses of sub-regions, which alleviates the training bias. Extensive experiments on four challenging datasets (ShanghaiTech Part A, UCF_CC_50, UCF-QNRF, and WorldExpo'10) demonstrate the superiority of the proposed approach.

/pdf/attention-scaling-for-crowd-counting-295ya8dm04.pdf

Attention Scaling for Crowd Counting

Crowd counting is receiving rapidly growing research interests due to its potential application value in numerous real-world scenarios. However, due to various challenges such as occlusion, insufficient resolution and dynamic backgrounds, crowd counting remains an unsolved problem in computer vision. Density estimation is a popular strategy for crowd counting, where conventional density estimation methods perform pixel-wise regression without explicitly accounting the interdependence of pixels. As a result, independent pixel-wise predictions can be noisy and inconsistent. In order to address such an issue, we propose a Relational Attention Network (RANet) with a self-attention mechanism for capturing interdependence of pixels. The RANet enhances the self-attention mechanism by accounting both short-range and long-range interdependence of pixels, where we respectively denote these implementations as local self-attention (LSA) and global self-attention (GSA). We further introduce a relation module to fuse LSA and GSA to achieve more informative aggregated feature representations. We conduct extensive experiments on four public datasets, including ShanghaiTech A, ShanghaiTech B, UCF-CC-50 and UCF-QNRF. Experimental results on all datasets suggest RANet consistently reduces estimation errors and surpasses the state-of-the-art approaches by large margins.

/pdf/relational-attention-network-for-crowd-counting-3gof1lspg3.pdf

Relational Attention Network for Crowd Counting

In crowd counting, each training image contains multiple people, where each person is annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth each annotated dot or to estimate the likelihood of every pixel given the annotated point. In this paper, we show that imposing Gaussians to annotations hurts generalization performance. Instead, we propose to use Distribution Matching for crowd COUNTing (DM-Count). In DM-Count, we use Optimal Transport (OT) to measure the similarity between the normalized predicted density map and the normalized ground truth density map. To stabilize OT computation, we include a Total Variation loss in our model. We show that the generalization error bound of DM-Count is tighter than that of the Gaussian smoothed methods. In terms of Mean Absolute Error, DM-Count outperforms the previous state-of-the-art methods by a large margin on two large-scale counting datasets, UCF-QNRF and NWPU, and achieves the state-of-the-art results on the ShanghaiTech and UCF-CC50 datasets. Notably, DM-Count ranked first on the leaderboard for the NWPU benchmark, reducing the error of the state-of-the-art published result by approximately 16%. Code is available at this https URL.

/pdf/distribution-matching-for-crowd-counting-k2yi9xx2fz.pdf

Distribution Matching for Crowd Counting

Accurately estimating the number of objects in a single image is a challenging yet meaningful task and has been applied in many applications such as urban planning and public safety. In the various object counting tasks, crowd counting is particularly prominent due to its specific significance to social security and development. Fortunately, the development of the techniques for crowd counting can be generalized to other related fields such as vehicle counting and environment survey, if without taking their characteristics into account. Therefore, many researchers are devoting to crowd counting, and many excellent works of literature and works have spurted out. In these works, they are must be helpful for the development of crowd counting. However, the question we should consider is why they are effective for this task. Limited by the cost of time and energy, we cannot analyze all the algorithms. In this paper, we have surveyed over 220 works to comprehensively and systematically study the crowd counting models, mainly CNN-based density map estimation methods. Finally, according to the evaluation metrics, we select the top three performers on their crowd counting datasets and analyze their merits and drawbacks. Through our analysis, we expect to make reasonable inference and prediction for the future development of crowd counting, and meanwhile, it can also provide feasible solutions for the problem of object counting in other fields. We provide the density maps and prediction results of some mainstream algorithm in the validation set of NWPU dataset for comparison and testing. Meanwhile, density map generation and evaluation tools are also provided. All the codes and evaluation results are made publicly available at this https URL.

pdf/cnn-based-density-estimation-and-crowd-counting-a-survey-2fuvqcqneo.pdf

CNN-based Density Estimation and Crowd Counting: A Survey

One of the critical challenges of object counting is the dramatic scale variations, which is introduced by arbitrary perspectives. We propose a reverse perspective network to solve the scale variations of input images, instead of generating perspective maps to smooth final outputs. The reverse perspective network explicitly evaluates the perspective distortions, and efficiently corrects the distortions by uniformly warping the input images. Then the proposed network delivers images with similar instance scales to the regressor. Thus the regression network doesn't need multi-scale receptive fields to match the various scales. Besides, to further solve the scale problem of more congested areas, we enhance the corresponding regions of ground-truth with the evaluation errors. Then we force the regressor to learn from the augmented ground-truth via an adversarial process. Furthermore, to verify the proposed model, we collected a vehicle counting dataset based on Unmanned Aerial Vehicles (UAVs). The proposed dataset has fierce scale variations. Extensive experimental results on four benchmark datasets show the improvements of our method against the state-of-the-arts.

/pdf/reverse-perspective-network-for-perspective-aware-object-z6z88og16d.pdf

Reverse Perspective Network for Perspective-Aware Object Counting

Crowd counting has recently generated huge popularity in computer vision, and is extremely challenging due to the huge scale variations of objects. In this paper, we propose the Attentional Neural Field (ANF) for crowd counting via density estimation. Within the encoder-decoder network, we introduce conditional random fields (CRFs) to aggregate multi-scale features, which can build more informative representations. To better model pair-wise potentials in CRFs, we incorperate non-local attention mechanism implemented as inter- and intra-layer attentions to expand the receptive field to the entire image respectively within the same layer and across different layers, which captures long-range dependencies to conquer huge scale variations. The CRFs coupled with the attention mechanism are seamlessly integrated into the encoder-decoder network, establishing an ANF that can be optimized end-to-end by back propagation. We conduct extensive experiments on four public datasets, including ShanghaiTech, WorldEXPO 10, UCF-CC-50 and UCF-QNRF. The results show that our ANF achieves high counting performance, surpassing most previous methods.

/pdf/attentional-neural-fields-for-crowd-counting-xf9b752nyv.pdf

Attentional Neural Fields for Crowd Counting

Zero-shot Learning (ZSL) aims to learn a classifier to recognize unseen categories without training samples. Most ZSL works based on embedding models handle the visual space and the semantic space through a common metric space and then apply a simple nearest neighbor search which directly leads to the hubness problem, one of the main challenges of ZSL. Contrary to recent works, whose conclusions about hubs are drawn based on Euclidean and specific models like ridge regression, we adopt cosine metric and for the first time prove cosine is model-agnostic to alleviate the hubness problem in ZSL. Assuming that the normalized mapped semantic vectors follow a uniform distribution, we provide theoretical analysis which demonstrates that hubs can be better reduced with a higher-dimensional cosine metric space. Moreover, we introduce a diversity-based regularizer with the cosine metric which underpins the assumption about the uniform distribution and further improves the model’s discriminative ability. Extensive experiments on five benchmarks and large-scale Imagenet dataset show that our method can improve the performance, surpassing previous embedding methods by large margins.

/pdf/model-agnostic-metric-for-zero-shot-learning-2ppmjq49pw.pdf

Model-Agnostic Metric for Zero-Shot Learning

Crowd counting is a popular topic with widespread applications. Currently, the biggest challenge to crowd counting is large-scale variation in objects. In this article, we focus on overcoming this challenge by proposing a novel Attentive Encoder-Decoder Network (AEDN), which is supervised on multiple feature scales to conduct crowd counting via density estimation. This work has three main contributions. First, we augment the traditional encoder-decoder architecture with our proposed residual attention blocks, which, beyond skip-connected encoded features, further extend the decoded features with attentive features. AEDN is better at establishing long-range dependencies between the encoder and decoder, therefore promoting more effective fusion of multi-scale features for handling scale-variations. Second, we design a new KL-divergence-based distribution loss to supervise the scale-aware structural differences between two density maps, which complements the pixel-isolated MSE loss and better optimizes AEDN to generate high-quality density maps. Third, we adopt a multi-scale supervision scheme, such that multiple KL divergences and MSE losses are deployed at all decoding stages, providing more thorough supervisions for different feature scales. Extensive experimental results on four public datasets, including ShanghaiTech Part A, ShanghaiTech Part B, UCF-CC-50, and UCF-QNRF, reveal the superiority and efficacy of the proposed method, which outperforms most state-of-the-art competitors.

Multi-scale Supervised Attentive Encoder-Decoder Network for Crowd Counting

Face alignment has been extensively researched in computer vision while remaining a challenging task. Direct face alignment based on convolutional neural networks (CNN) without relying on cascaded regression has recently emerged and achieved promising performance. In this paper, we propose a multi-scale aggregation network (MAN) for direct face alignment by aggregating features from intermediate layers of a CNN. Specifically, MAN adopts a new convolutional architecture to aggregate features at all scales in different semantic levels, which establishes highly informative facial representations for accurate alignment. Moreover, we introduce the attention mechanism into the network, which drives it to focus on the spatial regions closely related to facial landmarks for further improved performance. Our MAN achieves a general end-to-end learning architecture for multi-scale feature aggregation, which, coupled with spatial attention mechanism, is well-suited for direct face alignment. Extensive experiments conducted on four benchmark datasets, including AFLW, 300W, CelebA and 300VW, show that MAN consistently produces high performance and surpasses several state-of-the-art methods in most cases.

Multi-Scale Aggregation Network for Direct Face Alignment

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