2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2015)
Boston, MA, USA
June 7, 2015 to June 12, 2015
Santi Segui , Dept. Matematica Aplicada i Analisis, Universitat de Barcelona, Spain
Oriol Pujol , Dept. Matematica Aplicada i Analisis, Universitat de Barcelona, Spain
Jordi Vitria , Dept. Matematica Aplicada i Analisis, Universitat de Barcelona, Spain
Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene.
Feature extraction, Training, Supervised learning, Proposals, Accuracy, Visualization, Neural networks
S. Segui, O. Pujol and J. Vitria, "Learning to count with deep object features," 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Boston, MA, USA, 2015, pp. 90-96.