We have created a large diverse set of cars from overhead images, which are useful for training a deep learner to binary classify, detect and count them. The dataset and all related material will be made publically available. The set contains contextual matter to aid in identification of difficult targets. We demonstrate classification and detection on this dataset using a neural network we call ResCeption. This network combines residual learning with Inception-style layers and is used to count cars in one look. This is a new way to count objects rather than by localization or density estimation. It is fairly accurate, fast and easy to implement. Additionally, the counting method is not car or scene specific. It would be easy to train this method to count other kinds of objects and counting over new scenes requires no extra set up or assumptions about object locations.
Our method uses another approach. We teach a deep learning neural network to recognize the number of cars in an extended patch. It is trained only to count the number of objects as a class and is not given information about location or expected features. Then we count all the cars in a scene using a very large stride by counting them in groups at each stride location. This allows us to take one look at a large location and count by appearance. It has recently been demonstrated that one-look methods can excel at both speed and accuracy @cite_21 for recognition and localization. The idea of using a one-look network counter to “learn to count” has recently been demonstrated on synthetic data patches @cite_6 and by regression on subsampled crowd patches @cite_24 . Here we utilize a more robust network, and demonstrate that a large strided scan can be used to quickly count a very large scene with reasonable accuracy. Additionally, we are not constrained by scene or location. Cars can be automatically counted anywhere in the world, even if they are not on roads or moving.
@cite_24 @cite_21 @cite_6
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Abstract This paper aims to develop a simple but effective method that can estimate the number of people in still images. Inspired by the successful applications of deep learning and the appearance of crowd, we design a count-net based on Convolutional Neural Network (CNN). The count-net takes the appearance of crowd as auxiliary mechanism, thus filtering out most of the backgrounds and focusing more on peoples heads. In addition, we adopt a separated-aggregated framework since the structure of crowd in an image is rarely uniform. Firstly, we separate the crowd image into patches to treat different spatial locations discriminatively. Afterwards, these patches and the manual marked labels are fed into a count-net to train the parameters of this network. For practical application, only the counting channel of the count-net is demanded. Finally, the numbers of people in patches that belong to one image are summed as the output of the whole framework. Experimental results conducted on UCF and AHU-CROWD datasets demonstrate the superiority of our proposed method. 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. 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.