Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions

Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images. Albeit successful, vision-based crowd counting approaches could fail to capture informative features in extreme conditions, e.g., imaging at night and occlusion. In this work, we introduce a novel task of audiovisual crowd counting, in which visual and auditory information are integrated for counting purposes. We collect a large-scale benchmark, named auDiovISual Crowd cOunting (DISCO) dataset, consisting of 1,935 images and the corresponding audio clips, and 170,270 annotated instances. In order to fuse the two modalities, we make use of a linear feature-wise fusion module that carries out an affine transformation on visual and auditory features. Finally, we conduct extensive experiments using the proposed dataset and approach. Experimental results show that introducing auditory information can benefit crowd counting under different illumination, noise, and occlusion conditions. The dataset and code will be released. Code and data have been made availabl

PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting

Crowd counting, i.e., estimating the number of people in a crowded area, has attracted much interest in the research community. Although many attempts have been reported, crowd counting remains an open real-world problem due to the vast scale variations in crowd density within the interested area, and severe occlusion among the crowd. In this paper, we propose a novel Pyramid Density-Aware Attention-based network, abbreviated as PDANet, that leverages the attention, pyramid scale feature and two branch decoder modules for density-aware crowd counting. The PDANet utilizes these modules to extract different scale features, focus on the relevant information, and suppress the misleading ones. We also address the variation of crowdedness levels among different images with an exclusive Density-Aware Decoder (DAD). For this purpose, a classifier evaluates the density level of the input features and then passes them to the corresponding high and low crowded DAD modules. Finally, we generate an overall density map by considering the summation of low and high crowded density maps as spatial attention. Meanwhile, we employ two losses to create a precise density map for the input scene. Extensive evaluations conducted on the challenging benchmark datasets well demonstrate the superior performance of the proposed PDANet in terms of the accuracy of counting and generated density maps over the well-known state of the arts

TasselNet: Counting maize tassels in the wild via local counts regression network

Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment.Comment: 14 page

Deconstructing triplet nucleon-nucleon scattering

Nucleon-nucleon scattering in spin-triplet channels is analysed within an effective field theory where one-pion exchange is treated nonperturbatively. Justifying this requires the identification of an additional low-energy scale in the strength of that potential. Short-range interactions are organised according to the resulting power counting, in which the leading term is promoted to significantly lower order than in the usual perturbative counting. In each channel there is a critical momentum above which the waves probe the singular core of the tensor potential and the new counting is necessary. When the effects of one- and two-pion exchange have been removed using a distorted-wave Born approximation, the residual scattering in waves with L<=2 is well described by the first three terms in the new counting. In contrast, the scattering in waves with L>=3 is consistent with the perturbative counting, at least for energies up to 300 MeV. This pattern is in agreement with estimates of the critical momenta in these channels.Comment: 13 pages, RevTeX, 8 figures, minor clarifications adde

Baryon self energies in the chiral loop expansion

We compute the self energies of the baryon octet and decuplet states at the one-loop level applying the manifestly covariant chiral Lagrangian. It is demonstrated that expressions consistent with the expectation of power counting rules arise if the self energies are decomposed according to the Passarino-Veltman scheme supplemented by a minimal subtraction. This defines a partial summation of the chiral expansion. A finite renormalization required to install chiral power counting rules leads to the presence of an infrared renormalization scale. Good convergence properties for the chiral loop expansion of the baryon octet and decuplet masses are obtained for natural values of the infrared scale. A prediction for the strange-quark matrix element of the nucleon is made.Comment: 36 pages, 4 figures, 8 tables. The revised manuscript contains a proof that given any one-loop integral that arises when computing one-baryon processes it is sufficient to renormalize the scalar master-loop functions of the Passarino-Veltman reduction in a manner that the latter are compatible with the expectation of chiral counting rule