Automatic learning of gait signatures for people identification

This work targets people identification in video based on the way they walk (i.e. gait). While classical methods typically derive gait signatures from sequences of binary silhouettes, in this work we explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). We carry out a thorough experimental evaluation of the proposed CNN architecture on the challenging TUM-GAID dataset. The experimental results indicate that using spatio-temporal cuboids of optical flow as input data for CNN allows to obtain state-of-the-art results on the gait task with an image resolution eight times lower than the previously reported results (i.e. 80x60 pixels).Comment: Proof of concept paper. Technical report on the use of ConvNets (CNN) for gait recognition. Data and code: http://www.uco.es/~in1majim/research/cnngaitof.htm

String Equations for the Unitary Matrix Model and the Periodic Flag Manifold

The periodic flag manifold (in the Sato Grassmannian context) description of the modified Korteweg--de Vries hierarchy is used to analyse the translational and scaling self--similar solutions of this hierarchy. These solutions are characterized by the string equations appearing in the double scaling limit of the symmetric unitary matrix model with boundary terms. The moduli space is a double covering of the moduli space in the Sato Grassmannian for the corresponding self--similar solutions of the Korteweg--de Vries hierarchy, i.e. of stable 2D quantum gravity. The potential modified Korteweg--de Vries hierarchy, which can be described in terms of a line bundle over the periodic flag manifold, and its self--similar solutions corresponds to the symmetric unitary matrix model. Now, the moduli space is in one--to--one correspondence with a subset of codimension one of the moduli space in the Sato Grassmannian corresponding to self--similar solutions of the Korteweg--de Vries hierarchy.Comment: 21 pages in LaTeX-AMSTe

Parallelization of an algorithm for the automatic detection of deformable objects

This work presents the parallelization of an algorithm for the detection of deformable objects in digital images. The parallelization has been implemented with the message passing paradigm, using a master-slave model. Two versions have been developed, with synchronous and asynchronous communications

The multicomponent 2D Toda hierarchy: Discrete flows and string equations

The multicomponent 2D Toda hierarchy is analyzed through a factorization problem associated to an infinite-dimensional group. A new set of discrete flows is considered and the corresponding Lax and Zakharov--Shabat equations are characterized. Reductions of block Toeplitz and Hankel bi-infinite matrix types are proposed and studied. Orlov--Schulman operators, string equations and additional symmetries (discrete and continuous) are considered. The continuous-discrete Lax equations are shown to be equivalent to a factorization problem as well as to a set of string equations. A congruence method to derive site independent equations is presented and used to derive equations in the discrete multicomponent KP sector (and also for its modification) of the theory as well as dispersive Whitham equations.Comment: 27 pages. In the revised paper we improved the presentatio

Gait recognition and fall detection with inertial sensors

In contrast to visual information that is recorded by cameras placed somewhere, inertial information can be obtained from mobile phones that are commonly used in daily life. We present in this talk a general deep learning approach for gait and soft biometrics (age and gender) recognition. Moreover, we also study the use of gait information to detect actions during walking, specifically, fall detection. We perform a thorough experimental evaluation of the proposed approach on different datasets: OU-ISIR Biometric Database, DFNAPAS, SisFall, UniMiB-SHAR and ASLH. The experimental results show that inertial information can be used for gait recognition and fall detection with state-of-the-art results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Gait recognition applying Incremental learning

when new knowledge needs to be included in a classifier, the model is retrained from scratch using a huge training set that contains all available information of both old and new knowledge. However, in this talk, we present a way to include new information in a previously trained model without training from scratch and using a small subset of old data. We perform a thorough experimental evaluation of the proposed approach on two image classification datasets: CIFAR-100 and ImageNet. The experiment results show that it is possible to include new knowledge in a model without forgetting the previous one, although, the performance is still lower than training from scratch with the complete training set.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech