Crowd counting is one of the core tasks in various surveillance applications. A practical system involves estimating accurate head counts in dynamic scenarios under different lightning, camera perspective and occlusion states. Previous approaches estimate head counts despite that they can vary dramatically in different density settings; the crowd is often unevenly distributed and the results are therefore unsatisfactory. In this paper, we propose a lightweight deep learning framework that can automatically estimate the crowd density level and adaptively choose between different counter networks that are explicitly trained for different density domains. Experiments on two recent crowd counting datasets, UCF_CC_50 and ShanghaiTech, show that the proposed mechanism achieves promising improvements over state-of-the-art methods. Moreover, runtime speed is 20 FPS on a single GPU.
. Regression-based approaches such as @cite_8 @cite_6 @cite_28 @cite_35 @cite_34 @cite_5 @cite_3 are proposed to bypass the occlusion problem that can be critical for detection-based methods. Specifically, a mapping between image features and the head count is recovered, and the system benefits from better feature extraction and count number regression algorithms @cite_6 @cite_1 @cite_34 @cite_8 @cite_16 . Moreover, @cite_9 @cite_31 @cite_38 @cite_39 leverage spatial or depth information and use segmentation methods to filter the background region and regress count numbers only on foreground segments. These type of methods are sensitive to different crowd density levels and heavily depend on a normalization strategy that is universally good.
@cite_35 @cite_38 @cite_8 @cite_28 @cite_9 @cite_1 @cite_6 @cite_3 @cite_16 @cite_39 @cite_5 @cite_31 @cite_34
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Our objective is to count (and localize) object instances in an image interactively. We target the regime where individual object detectors do not work reliably due to crowding, or overlap, or size of the instances, and take the approach of estimating an object density. An approach to the problem of estimating the size of inhomogeneous crowds, which are composed of pedestrians that travel in different directions, without using explicit object segmentation or tracking is proposed. Instead, the crowd is segmented into components of homogeneous motion, using the mixture of dynamic-texture motion model. A set of holistic low-level features is extracted from each segmented region, and a function that maps features into estimates of the number of people per segment is learned with Bayesian regression. Two Bayesian regression models are examined. The first is a combination of Gaussian process regression with a compound kernel, which accounts for both the global and local trends of the count mapping but is limited by the real-valued outputs that do not match the discrete counts. We address this limitation with a second model, which is based on a Bayesian treatment of Poisson regression that introduces a prior distribution on the linear weights of the model. Since exact inference is analytically intractable, a closed-form approximation is derived that is computationally efficient and kernelizable, enabling the representation of nonlinear functions. An approximate marginal likelihood is also derived for kernel hyperparameter learning. The two regression-based crowd counting methods are evaluated on a large pedestrian data set, containing very distinct camera views, pedestrian traffic, and outliers, such as bikes or skateboarders. Experimental results show that regression-based counts are accurate regardless of the crowd size, outperforming the count estimates produced by state-of-the-art pedestrian detectors. Results on 2 h of video demonstrate the efficiency and robustness of the regression-based crowd size estimation over long periods of time. Poisson regression models the noisy output of a counting function as a Poisson random variable, with a log-mean parameter that is a linear function of the input vector. In this work, we analyze Poisson regression in a Bayesian setting, by introducing a prior distribution on the weights of the linear function. Since exact inference is analytically unobtainable, we derive a closed-form approximation to the predictive distribution of the model. We show that the predictive distribution can be kernelized, enabling the representation of non-linear log-mean functions. We also derive an approximate marginal likelihood that can be optimized to learn the hyperparameters of the kernel. We then relate the proposed approximate Bayesian Poisson regression to Gaussian processes. Finally, we present experimental results using Bayesian Poisson regression for crowd counting from low-level features. We present a privacy-preserving system for estimating the size of inhomogeneous crowds, composed of pedestrians that travel in different directions, without using explicit object segmentation or tracking. First, the crowd is segmented into components of homogeneous motion, using the mixture of dynamic textures motion model. Second, a set of simple holistic features is extracted from each segmented region, and the correspondence between features and the number of people per segment is learned with Gaussian process regression. We validate both the crowd segmentation algorithm, and the crowd counting system, on a large pedestrian dataset (2000 frames of video, containing 49,885 total pedestrian instances). Finally, we present results of the system running on a full hour of video. Cross-scene regression tasks, such as congestion level detection and crowd counting, are useful but challenging. There are two main problems, which limit the performance of existing algorithms. The first one is that no appropriate congestion-related feature can reflect the real density in scenes. Though deep learning has been proved to be capable of extracting high level semantic representations, it is hard to converge on regression tasks, since the label is too weak to guide the learning of parameters in practice. Thus, many approaches utilize additional information, such as a density map, to guide the learning, which increases the effort of labeling. Another problem is that most existing methods are composed of several steps, for example, feature extraction and regression. Since the steps in the pipeline are separated, these methods face the problem of complex optimization. To remedy it, a deep metric learning-based regression method is proposed to extract density related features, and learn better distance measurement simultaneously. The proposed networks trained end-to-end for better optimization can be used for crowdedness regression tasks, including congestion level detection and crowd counting. Extensive experiments confirm the effectiveness of the proposed method. Real-time accurate crowd counting is one of important tasks in intelligent visual surveillance systems. Most previous works can only count passing people robustly without heavy occlusions which are very common in the practical surveillance scenes. To solve this difficult problem, we propose a new method for crowd counting for RGB-D (RGB plus depth) data using a commodity depth camera. In our method, we first detect each head-shoulder of the passing or still person in the surveillance region with fast template matching based on depth information including pedestrian filling with convex hull segmentation. Then, we track and count each detected head-shoulder based on RGB information bidirectionally. By using this approach, we have built a practical system for robust and fast crowd counting. Extensive experimental results show that our method achieves significant improvement comparing to states-of-the-art approach, and the built system is not only robust to heavy occlusions, but also can be deployed in the real time crowd counting application scenes. Crowd counting is a very challenging task in crowded scenes due to heavy occlusions, appearance variations and perspective distortions. Current crowd counting methods typically operate on an image patch level with overlaps, then sum over the patches to get the final count. In this paper, we propose an end-to-end convolutional neural network (CNN) architecture that takes a whole image as its input and directly outputs the counting result. While making use of sharing computations over overlapping regions, our method takes advantages of contextual information when predicting both local and global count. In particular, we first feed the image to a pre-trained CNN to get a set of high level features. Then the features are mapped to local counting numbers using recurrent network layers with memory cells. We perform the experiments on several challenging crowd counting datasets, which achieve the state-of-the-art results and demonstrate the effectiveness of our method.