This paper aims to develop a method than can accurately estimate the crowd count from an individual image with arbitrary crowd density and arbitrary perspective. To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map. The proposed MCNN allows the input image to be of arbitrary size or resolution. By utilizing filters with receptive fields of different sizes, the features learned by each column CNN are adaptive to variations in people/head size due to perspective effect or image resolution. Furthermore, the true density map is computed accurately based on geometry-adaptive kernels which do not need knowing the perspective map of the input image. Since exiting crowd counting datasets do not adequately cover all the challenging situations considered in our work, we have collected and labelled a large new dataset that includes 1198 images with about 330,000 heads annotated. On this challenging new dataset, as well as all existing datasets, we conduct extensive experiments to verify the effectiveness of the proposed model and method. In particular, with the proposed simple MCNN model, our method outperforms all existing methods. In addition, experiments show that our model, once trained on one dataset, can be readily transferred to a new dataset.

/pdf/single-image-crowd-counting-via-multi-column-convolutional-2h5l5iatou.pdf

Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

Cross-scene crowd counting is a challenging task where no laborious data annotation is required for counting people in new target surveillance crowd scenes unseen in the training set. The performance of most existing crowd counting methods drops significantly when they are applied to an unseen scene. To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count. This proposed switchable learning approach is able to obtain better local optimum for both objectives. To handle an unseen target crowd scene, we present a data-driven method to finetune the trained CNN model for the target scene. A new dataset including 108 crowd scenes with nearly 200,000 head annotations is introduced to better evaluate the accuracy of cross-scene crowd counting methods. Extensive experiments on the proposed and another two existing datasets demonstrate the effectiveness and reliability of our approach.

/pdf/cross-scene-crowd-counting-via-deep-convolutional-neural-3k01mli6ln.pdf

Cross-scene crowd counting via deep convolutional neural networks

We propose to leverage multiple sources of information to compute an estimate of the number of individuals present in an extremely dense crowd visible in a single image. Due to problems including perspective, occlusion, clutter, and few pixels per person, counting by human detection in such images is almost impossible. Instead, our approach relies on multiple sources such as low confidence head detections, repetition of texture elements (using SIFT), and frequency-domain analysis to estimate counts, along with confidence associated with observing individuals, in an image region. Secondly, we employ a global consistency constraint on counts using Markov Random Field. This caters for disparity in counts in local neighborhoods and across scales. We tested our approach on a new dataset of fifty crowd images containing 64K annotated humans, with the head counts ranging from 94 to 4543. This is in stark contrast to datasets used for existing methods which contain not more than tens of individuals. We experimentally demonstrate the efficacy and reliability of the proposed approach by quantifying the counting performance.

/pdf/multi-source-multi-scale-counting-in-extremely-dense-crowd-49fezp9o6o.pdf

Multi-source Multi-scale Counting in Extremely Dense Crowd Images

We propose a novel crowd counting model that maps a given crowd scene to its density. Crowd analysis is compounded by myriad of factors like inter-occlusion between people due to extreme crowding, high similarity of appearance between people and background elements, and large variability of camera view-points. Current state-of-the art approaches tackle these factors by using multi-scale CNN architectures, recurrent networks and late fusion of features from multi-column CNN with different receptive fields. We propose switching convolutional neural network that leverages variation of crowd density within an image to improve the accuracy and localization of the predicted crowd count. Patches from a grid within a crowd scene are relayed to independent CNN regressors based on crowd count prediction quality of the CNN established during training. The independent CNN regressors are designed to have different receptive fields and a switch classifier is trained to relay the crowd scene patch to the best CNN regressor. We perform extensive experiments on all major crowd counting datasets and evidence better performance compared to current state-of-the-art methods. We provide interpretable representations of the multichotomy of space of crowd scene patches inferred from the switch. It is observed that the switch relays an image patch to a particular CNN column based on density of crowd.

/pdf/switching-convolutional-neural-network-for-crowd-counting-2m8qvpq8aj.pdf

Switching Convolutional Neural Network for Crowd Counting

With multiple crowd gatherings of millions of people every year in events ranging from pilgrimages to protests, concerts to marathons, and festivals to funerals; visual crowd analysis is emerging as a new frontier in computer vision. In particular, counting in highly dense crowds is a challenging problem with far-reaching applicability in crowd safety and management, as well as gauging political significance of protests and demonstrations. In this paper, we propose a novel approach that simultaneously solves the problems of counting, density map estimation and localization of people in a given dense crowd image. Our formulation is based on an important observation that the three problems are inherently related to each other making the loss function for optimizing a deep CNN decomposable. Since localization requires high-quality images and annotations, we introduce UCF-QNRF dataset that overcomes the shortcomings of previous datasets, and contains 1.25 million humans manually marked with dot annotations. Finally, we present evaluation measures and comparison with recent deep CNNs, including those developed specifically for crowd counting. Our approach significantly outperforms state-of-the-art on the new dataset, which is the most challenging dataset with the largest number of crowd annotations in the most diverse set of scenes.

/pdf/composition-loss-for-counting-density-map-estimation-and-50holt9bk1.pdf

Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds

This paper presents a multi-output regression model for crowd counting in public scenes. Existing counting by regression methods either learn a single model for global counting, or train a large number of separate regressors for localised density estimation. In contrast, our single regression model based approach is able to estimate people count in spatially localised regions and is more scalable without the need for training a large number of regressors proportional to the number of local regions. In particular, the proposed model automatically learns the functional mapping between interdependent low-level features and multi-dimensional structured outputs. The model is able to discover the inherent importance of different features for people counting at different spatial locations. Extensive evaluations on an existing crowd analysis benchmark dataset and a new more challenging dataset demonstrate the effectiveness of our approach.

/pdf/feature-mining-for-localised-crowd-counting-4rcuyb2a31.pdf

Feature mining for localised crowd counting

The key challenge for learning a fine-grained sketch-based image retrieval (FG-SBIR) model is to bridge the domain gap between photo and sketch. Existing models learn a deep joint embedding space with discriminative losses where a photo and a sketch can be compared. In this paper, we propose a novel discriminative-generative hybrid model by introducing a generative task of cross-domain image synthesis. This task enforces the learned embedding space to preserve all the domain invariant information that is useful for cross-domain reconstruction, thus explicitly reducing the domain gap as opposed to existing models. Extensive experiments on the largest FG-SBIR dataset Sketchy [19] show that the proposed model significantly outperforms state-of-the-art discriminative FG-SBIR models.

/pdf/cross-domain-generative-learning-for-fine-grained-sketch-3m38vo2qzv.pdf

Cross-domain Generative Learning for Fine-Grained Sketch-Based Image Retrieval

Fine-grained image retrieval (FGIR) enables a user to search for a photo of an object instance based on a mental picture. Depending on how the object is described by the user, two general approaches exist: sketch-based FGIR or text-based FGIR, each of which has its own pros and cons. However, no attempt has been made to systematically investigate how informative each of these two input modalities is, and more importantly whether they are complementary to each thus should be modelled jointly. In this work, for the first time we introduce a multi-modal FGIR dataset with both sketches and sentences description provided as query modalities. A multi-modal quadruplet deep network is formulated to jointly model the sketch and text input modalities as well as the photo output modality. We show that on its own the sketch modality is much more informative than text and each modality can benefit the other when they are modelled jointly.

/pdf/fine-grained-image-retrieval-the-text-sketch-input-dilemma-7ico3030ol.pdf

Tony Xiang

Papers

Feature mining for localised crowd counting

Cross-domain Generative Learning for Fine-Grained Sketch-Based Image Retrieval

Fine-Grained Image Retrieval: the Text/Sketch Input Dilemma

2012 IEEE Conference on Computer Vision and Pattern Recognition