Human Behaviour Recognition based on Trajectory Analysis using Neural Networks

Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Description Vector (ADV) based on the number of occurrences of a person is in a specific point of the scenario and the local movements that perform in it. The ADV is calculated for each cell of the scenario in which it is spatially sampled obtaining a cue for different clustering methods. The ADV representation has been tested as the input of several classic classifiers and compared to other approaches using CAVIAR dataset sequences obtaining great accuracy in the recognition of the behaviour of people in a Shopping Centre.

RGBD Human-Hand recognition for the Interaction with Robot-Hand

New low cost sensors and the new open free libraries for 3D image processing are permitting to achieve important advances for robot vision applications such as tridimensional object recognition, semantic mapping, navigation and localization of robots, human detection and/or gesture recognition for human-machine interaction. In this paper, a method to recognize the human hand and to track the fingers is proposed. This new method is based on point clouds from range images, RGBD. It does not require visual marks, camera calibration, environment knowledge and complex expensive acquisition systems. Furthermore, this method has been implemented to create a human interface in order to move a robot hand. The human hand is recognized and the movement of the fingers is analyzed. Afterwards, it is imitated from a Barret hand, using communication events programmed from ROS.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.

Football Video Annotation Based on Player Motion Recognition Using Enhanced Entropy

This paper presents a semi-parametric Algorithm for parsing football video structures. The approach works on a two interleaved based process that closely collaborate towards a common goal. The core part of the proposed method focus perform a fast automatic football video annotation by looking at the enhance entropy variance within a series of shot frames. The entropy is extracted on the Hue parameter from the HSV color system, not as a global feature but in spatial domain to identify regions within a shot that will characterize a certain activity within the shot period. The second part of the algorithm works towards the identification of dominant color regions that could represent players and playfield for further activity recognition. Experimental Results shows that the proposed football video segmentation algorithm performs with high accuracy.

Calcineurin expression and activity is regulated by the intracellular redox status and under hypertension in human neutrophils

Calcineurin (protein phosphatase 2B) (CN) comprises a family of serine/threonine phosphatases that play a pivotal role in signal transduction cascades in a variety of cells, including neutrophils. Angiotensin II (Ang II) increases both activity and de novo synthesis of CN in human neutrophils. This study focuses on the role that intracellular redox status plays in the induction of CN activity by Ang II. Both de novo synthesis of CN and activity increase promoted by Ang II were downregulated when cells were treated with l-buthionine-(S,R)-sulfoximine, an inhibitor of synthesis of the antioxidant glutathione. We have also investigated the effect of pyrrolidine dithiocarbamate and phenazine methosulfate, which are antioxidant and oxidant compounds, respectively, and concluded that the intracellular redox status of neutrophils is highly critical for Ang II-induced increase of CN expression and activity. Results obtained in neutrophils from hypertensive patients were very similar to those obtained in these cells on treatment with Ang II. We have also addressed the possible functional implication of CN activation in the development of hypertension. Present findings indicate that downregulation of hemoxygenase-1 expression in neutrophils from hypertensive subjects is likely mediated by CN, which acts by hindering translocation to the nucleus of the transcription factor NRF2. These data support and extend our previous results and those from other authors on modulation of CN expression and activity levels by the intracellular redox status.

Human-computer interaction for optical music recognition tasks

The need to digitise music scores has led to the development of Optical Music Recognition (OMR) tools. Unfortunately, the performance of these systems is still far from providing acceptable results. This situation forces the user to be involved in the process due to the need of correcting the mistakes made during recognition. However, this correction is performed over the output of the system, so these interventions are not exploited to improve the performance of the recognition. This work sets the scenario in which human and machine interact to accurately complete the OMR task with the least possible effort for the user.

Carbon and nitrogen limitation increase chitosan antifungal activity in Neurospora crassa and fungal human pathogens

Chitosan permeabilizes plasma membrane and kills sensitive filamentous fungi and yeast. Membrane fluidity and cell energy determine chitosan sensitivity in fungi. A five-fold reduction of both glucose (main carbon (C) source) and nitrogen (N) increased 2-fold Neurospora crassa sensitivity to chitosan. We linked this increase with production of intracellular reactive oxygen species (ROS) and plasma membrane permeabilization. Releasing N. crassa from nutrient limitation reduced chitosan antifungal activity in spite of high ROS intracellular levels. With lactate instead of glucose, C and N limitation increased N. crassa sensitivity to chitosan further (4-fold) than what glucose did. Nutrient limitation also increased sensitivity of filamentous fungi and yeast human pathogens to chitosan. For Fusarium proliferatum, lowering 100-fold C and N content in the growth medium, increased 16-fold chitosan sensitivity. Similar results were found for Candida spp. (including fluconazole resistant strains) and Cryptococcus spp. Severe C and N limitation increased chitosan antifungal activity for all pathogens tested. Chitosan at 100 μg ml-1 was lethal for most fungal human pathogens tested but non-toxic to HEK293 and COS7 mammalian cell lines. Besides, chitosan increased 90% survival of Galleria mellonella larvae infected with C. albicans. These results are of paramount for developing chitosan as antifungal.