Meeting detection in video through semantic analysis

In this paper we present a novel approach to detect people meeting. The proposed approach works by translating people behaviour from trajectory information into semantic terms. Having available a semantic model of the meeting behaviour, the event detection is performed in the semantic domain. The model is learnt employing a soft-computing clustering algorithm that combines trajectory information and motion semantic terms. A stable representation can be obtained from a series of examples. Results obtained on a series of videos with different types of meeting situations show that the proposed approach can learn a generic model that can effectively be applied on the behaviour recognition of meeting situations

PETS 2014: dataset and challenge

This paper describes the dataset and vision challenges that form part of the PETS 2014 workshop. The datasets are multisensor sequences containing different activities around a parked vehicle in a parking lot. The dataset scenarios were filmed from multiple cameras mounted on the vehicle itself and involve multiple actors. In PETS2014 workshop, 22 acted scenarios are provided of abnormal behaviour around the parked vehicle. The aim in PETS 2014 is to provide a standard benchmark that indicates how detection, tracking, abnormality and behaviour analysis systems perform against a common database. The dataset specifically addresses several vision challenges corresponding to different steps in a video understanding system: Low-Level Video Analysis (object detection and tracking), Mid-Level Video Analysis (‘simple’ event detection: the behaviour recognition of a single actor) and High-Level Video Analysis (‘complex’ event detection: the behaviour and interaction recognition of several actors)

Loitering behaviour detection of boats at sea

We present in this paper a technique for Loitering detection based on the analysis of activity zones of the monitored area. Activity zones are learnt online employing a soft computing-based algorithm which takes as input the trajectory of object mobiles appearing on the scene. Statistical properties on zone occupancy and transition between zones makes it possible to discover abnormalities without the need to learn abnormal models beforehand. We have applied this approch to the PETS2017 IPATCH dataset and addressed the challenge on detecting skiff boats loitering around a protected ship, which eventually is attacked by the skiffs. Our results show that we can detect the suspicious behaviour on time to trigger an early warning

Multicamera trajectory analysis for semantic behaviour characterisation

In this paper we propose an innovative approach for behaviour recognition, from a multicamera environment, based on translating video activity into semantics. First, we fuse tracks from individual cameras through clustering employing soft computing techniques. Then, we introduce a higher-level module able to translate fused tracks into semantic information. With our proposed approach, we address the challenge set in PETS 2014 on recognising behaviours of interest around a parked vehicle, namely the abnormal behaviour of someone walking around the vehicle

PETS 2017: dataset and challenge

This paper indicates the dataset and challenges evaluated under PETS2017. In this edition PETS continues the evaluation theme of on-board surveillance systems for protection of mobile critical assets as set in PETS 2016. The datasets include (1) the ARENA Dataset; an RGB camera dataset, as used for PETS2014 to PETS 2016, which addresses protection of trucks; and (2) the IPATCH Dataset; a multi sensor dataset, as used in PETS2016, addressing the application of multi sensor surveillance to protect a vessel at sea from piracy. The datasets allow for performance evaluation of tracking in low-density scenarios and detection of various surveillance events ranging from innocuous abnormalities to dangerous and criminal situations. Training data for tracking algorithms is released with the dataset; tracking data is also available for authors addressing only surveillance event detection challenges but not working on tracking

Caractérisation des propriétés thermoélectriques des composants en régime harmonique : techniques et modélisation

Ce travail propose une nouvelle approche pour la caractérisation de propriétés de transport de composants et matériaux thermoélectriques, basés sur l'analyse en régime harmonique des réponses thermique et électrique des composants. Ce régime a été peu étudié en thermoélectricité, à cause de la nature fortement non-linéaire des phénomènes thermoélectriques. Il présente cependant plusieurs avantages, d'une part, l'utilisation de systèmes de mesures synchrones, connus pour sa résolution et sa capacité de rejet au bruit ; d'autre part, la possibilité d'étudier l'intégralité de propriétés thermoélectriques : le coefficient de Seebeck, la conductivité thermique, la résistivité électrique, mais également la diffusivité thermique, et les résistances thermiques d'interface, ces dernières ont été rarement étudiées, puisque difficiles à mesurer. Les effets thermoélectriques ont été modélisés en régime oscillant en utilisant la méthode des quadripôles thermiques, permettant de prédire des grandeurs pertinentes de la réponse d'un couple thermoélectrique, telles que le champ de température et la tension aux bornes du composant. L'étude de la sensibilité du modèle a dévoilé des zones privilégiées pour l'extraction de propriétés du composant. Deux bancs de mesure équivalents sont proposés : # Un banc de mesure AFM thermique (SThM) pour la mesure du champ de température. Un modèle prenant en compte l'interaction entre la pointe SThM et l'échantillon a été développé pour effectuer le calibrage de l'instrument. # Un banc photo-thermique, où le composant est excité par une source laser qui balaye sa surface, induisant par échauffement une tension Seebeck, tension mesurée sur diverses positions du faisceau chauffant. Aux mesures obtenues sur les bancs mentionnés, sont associés des modèles aux quadripôles thermiques permetttant la caractérisation intégrale de composants thermoélectriques, des propriétés intrinsèques des matériaux qui le constituent, et des propriétés d'interfaces

On-line learning of activities from video

International audienceThe present work introduces a new method for activity extraction from video. To achieve this, we focus on the modelling of context by developing an algorithm that automatically learns the main activity zones of the observed scene by taking as input the trajectories of detected mobiles. Automatically learning the context of the scene (activity zones) allows first to extract a knowledge on the occupancy of the different areas of the scene. In a second step, learned zones are employed to extract people activities by relating mobile trajectories to the learned zones, in this way, the activity of a person can be summarised as the series of zones that the person has visited. For the analysis of the trajectory, a multi resolution analysis is set such that a trajectory is segmented into a series of tracklets based on changing speed points thus allowing differentiating when people stop to interact with elements of the scene or other persons. Tracklets allow thus to extract behavioural information. Starting and ending tracklet points are fed to a simple yet advantageous incremental clustering algorithm to create an initial partition of the scene. Similarity relations between resulting clusters are modelled employing fuzzy relations. These can then be aggregated with typical soft-computing algebra. A clustering algorithm based on the transitive closure calculation of the fuzzy relations allows building the final structure of the scene. To allow for incremental learning and update of activity zones (and thus people activities), fuzzy relations are defined with on-line learning terms. We present results obtained on real videos from different activity domains

Quantifying slumness with remote sensing data

The presence of slums in a city is an indicator of poverty and its proper delimitation is a matter of interest for researchers and policy makers. Socio-economic data from surveys and censuses are the primary source of information to identify and quantify slumness within a city or a town. One problem of using survey data for quantifying slumness is that this type of data is usually collected every ten years and is an expensive and time consuming process. Based on the premise that the physical appearance of an urban settlement is a reflection of the society that created it and on the assumption that people living in urban areas with similar physical housing conditions will have similar social and demographic characteristics (Jain, 2008; Taubenb¨ock et al., 2009b); this paper uses data from Medellin City, Colombia, to estimate slum index using solely remote sensing data from an orthorectified, pan-sharpened, natural color Quickbird scene. For Medellin city, the percentage of clay roofs cover and the mean swimming pool density at the analytical region level can explain up to 59% of the variability in the slum index. Structure and texture measures are useful to characterize the differences in the homogeneity of the spatial pattern of the urban layout and they improve the explanatory power of the statistical models when taken into account. When no other information is used, they can explain up to 30% of the variability of the slum index. The results of this research are encouraging and many researchers, urban planners and policy makers could benefit from this rapid and low cost approach to characterize the intra-urban variations of slumness in cities with sparse data or no data at all

Antibiotic resistance and virulence profiles of Gram-Negative bacteria isolated from loggerhead sea turtles (Caretta caretta) of the Island of Maio, Cape Verde

Research Areas: Infectious Diseases ; Pharmacology & PharmacyPrevious studies revealed high levels of antimicrobial resistance (AMR) in loggerhead sea turtles (Caretta caretta), describing this species as prime reservoir of antimicrobial-resistant bacteria. This study aimed to characterise, for the first time, the AMR and virulence profiles of Gram-negative bacteria isolated from 33 nesting loggerhead turtles of the island of Maio, Cape Verde. Cloacal, oral, and egg content swab samples (n = 99) were collected and analysed using conventional bacteriological techniques. Shewanella putrefaciens, Morganella morganii, and Vibrio alginolyticus were isolated from the samples under study. The isolates obtained from this loggerhead subpopulation (North-East Atlantic) revealed lower levels of AMR, compared with the results of studies performed in other subpopulations (e.g., Mediterranean). However, the detection of resistance to carbapenems and multiple antimicrobial resistance indices higher than 0.20, raises concern about the potential association of these animals to points of high antimicrobial exposure. Furthermore, virulence phenotypic characterisation revealed that the isolates presented complex virulence profiles, including the ability to produce biofilms. Finally, due to their pathogenic potential, and considering the evidence of illegal consumption of turtle-related products on the island of Maio, the identified bacteria may represent a significant threat to public health.info:eu-repo/semantics/publishedVersio

Corticomuscular synchronization with small and large dynamic force output

BACKGROUND: Over the last years much research has been devoted to investigating the synchronization between cortical motor and muscular activity as measured by EEG/MEG-EMG coherence. The main focus so far has been on corticomuscular coherence (CMC) during static force condition, for which coherence in beta-range has been described. In contrast, we showed in a recent study [1] that dynamic force condition is accompanied by gamma-range CMC. The modulation of the CMC by various dynamic force amplitudes, however, remained uninvestigated. The present study addresses this question. We examined eight healthy human subjects. EEG and surface EMG were recorded simultaneously. The visuomotor task consisted in isometric compensation for 3 forces (static, small and large dynamic) generated by a manipulandum. The CMC, the cortical EEG spectral power (SP), the EMG SP and the errors in motor performance (as the difference between target and exerted force) were analyzed. RESULTS: For the static force condition we found the well-documented, significant beta-range CMC (15-30Hz) over the contralateral sensorimotor cortex. Gamma-band CMC (30-45Hz) occurred in both small and large dynamic force conditions without any significant difference between both conditions. Although in some subjects beta-range CMC was observed during both dynamic force conditions no significant difference between conditions could be detected. With respect to the motor performance, the lowest errors were obtained in the static force condition and the highest ones in the dynamic condition with large amplitude. However, when we normalized the magnitude of the errors to the amplitude of the applied force (relative errors) no significant difference between both dynamic conditions was observed. CONCLUSIONS: These findings confirm that during dynamic force output the corticomuscular network oscillates at gamma frequencies. Moreover, we show that amplitude modulation of dynamic force has no effect on the gamma CMC in the low force range investigated. We suggest that gamma CMC is rather associated with the internal state of the sensorimotor system as supported by the unchanged relative error between both dynamic conditions