Online Tracking Parameter Adaptation based on Evaluation

Parameter tuning is a common issue for many tracking algorithms. In order to solve this problem, this paper proposes an online parameter tuning to adapt a tracking algorithm to various scene contexts. In an offline training phase, this approach learns how to tune the tracker parameters to cope with different contexts. In the online control phase, once the tracking quality is evaluated as not good enough, the proposed approach computes the current context and tunes the tracking parameters using the learned values. The experimental results show that the proposed approach improves the performance of the tracking algorithm and outperforms recent state of the art trackers. This paper brings two contributions: (1) an online tracking evaluation, and (2) a method to adapt online tracking parameters to scene contexts.Comment: IEEE International Conference on Advanced Video and Signal-based Surveillance (2013

Optimisation du suivi de personnes dans un réseau de caméras

This thesis addresses the problem of improving the performance of people tracking process in a new framework called Global Tracker, which evaluates the quality of people trajectory (obtained by simple tracker) and recovers the potential errors from the previous stage. The first part of this Global Tracker estimates the quality of the tracking results, based on a statistical model analyzing the distribution of the target features to detect potential anomalies.To differentiate real errors from natural phenomena, we analyze all the interactions between the tracked object and its surroundings (other objects and background elements). In the second part, a post tracking method is designed to associate different tracklets (segments of trajectory) corresponding to the same person which were not associated by a first stage of tracking. This tracklet matching process selects the most relevant appearance features to compute a visual signature for each tracklet. Finally, the Global Tracker is evaluated with various benchmark datasets reproducing real-life situations, outperforming the state-of-the-art trackers.Cette thèse s’intéresse à l’amélioration des performances du processus de suivi de personnes dans un réseau de caméras et propose une nouvelle plate-forme appelée global tracker. Cette plate-forme évalue la qualité des trajectoires obtenues par un simple algorithme de suivi et permet de corriger les erreurs potentielles de cette première étape de suivi. La première partie de ce global tracker estime la qualité des trajectoires à partir d’un modèle statistique analysant des distributions des caractéristiques de la cible (ie : l’objet suivi) telles que ses dimensions, sa vitesse, sa direction, afin de détecter de potentielles anomalies. Pour distinguer de véritables erreurs par rapport à des phénomènes optiques, nous analysons toutes les interactions entre l’objet suivi et tout son environnement incluant d’autres objets mobiles et les éléments du fond de la scène. Dans la deuxième partie du global tracker, une méthode en post-traitement a été conçue pour associer les différentes tracklets (ie : segments de trajectoires fiables) correspondant à la même personne qui n’auraient pas été associées correctement par la première étape de suivi. L’algorithme d’association des tracklets choisit les caractéristiques d’apparence les plus saillantes et discriminantes afin de calculer une signature visuelle adaptée à chaque tracklet. Finalement le global tracker est évalué à partir de plusieurs bases de données de benchmark qui reproduit une large variété de situations réelles. A travers toutes ces expérimentations, les performances du global tracker sont équivalentes ou supérieures aux meilleurs algorithmes de suivi de l’état de l’art

Global tracker: An online evaluation framework to improve tracking quality

International audienceEvaluating the quality of tracking outputs is an important task in video analysis. This paper presents a new framework for estimating both detection and tracking quality during runtime. If anomalies are detected in the tracking output results, they are categorized as natural phenomena or real errors using contextual information. As this framework should be generic and work on any kind of system (single camera, camera network), a re-acquisition step using a constrained clustering algorithm is also performed in order to keep track of the object even if it leaves the scene and comes back or appears on another camera. The framework is evaluated on two datasets using different kinds of tracking algorithms

Group Tracking and Behavior Recognition in Long Video Surveillance Sequences

International audienceThis paper makes use of recent advances in group tracking and behavior recognition to process large amounts of video surveillance data from an underground railway station and perform a statistical analysis. The most important advantages of our approach are the robustness to process long videos and the capacity to recognize several and different events at once. This analysis automatically brings forward data about the usage of the station and the various behaviors of groups in different hours of the day. This data would be very hard to obtain without an automatic group tracking and behavior recognition method. We present the results and interpretation of one month of processed data from a video surveillance camera in the Torino subway

Recovering people tracking errors using enhanced covariance-based signatures

International audienceThis paper presents a new approach for tracking multiple persons in a single camera. This approach focuses on re- covering tracked individuals that have been lost and are detected again, after being miss-detected (e.g. occluded) or after leaving the scene and coming back. In order to correct tracking errors, a multi-cameras re-identification method is adapted, with a real-time constraint. The proposed ap- proach uses a highly discriminative human signature based on covariance matrix, improved using background subtrac- tion, and a people detection confidence. The problem of linking several tracklets belonging to the same individual is also handled as a ranking problem using a learned pa- rameter. The objective is to create clusters of tracklets de- scribing the same individual. The evaluation is performed on PETS2009 dataset showing promising results

Preventing Corrosion of Aluminum Metal with Nanometer-Thick Films of Al2O3 Capped with TiO2 for Ultraviolet Plasmonics

Extending plasmonics into the ultraviolet range imposes the use of aluminum to achieve the best optical performance. However, water corrosion is a major limiting issue for UV aluminum plasmonics, as this phenomenon occurs significantly faster in presence of UV light, even at low laser powers of a few microwatts. Here we assess the performance of nanometer-thick layers of various metal oxides deposited by atomic layer deposition (ALD) and plasma-enhanced chemical vapor deposition (PECVD) on top of aluminum nanoapertures to protect the metal against UV photocorrosion. The combination of a 5 nm Al2O3 layer covered by a 5 nm TiO2 capping provides the best resistance performance, while a single 10 nm layer of SiO2 or HfO2 is a good alternative. We also report the influence of the laser wavelength, the laser operation mode and the pH of the solution. Properly choosing these conditions significantly extends the range of optical powers for which the aluminum nanostructures can be used. As application, we demonstrate the label-free detection of streptavidin proteins with improved signal to noise ratio. Our approach is also beneficial to promote the long-term stability of the aluminum nanostructures. Finding the appropriate nanoscale protection against aluminum corrosion is the key to enable the development of UV plasmonic applications in chemistry and biology

Event Recognition System for Older People Monitoring Using an RGB-D Camera

International audienceIn many domains such as health monitoring, the semantic information provided by automatic monitoring systems has become essential. These systems should be as robust, as easy to deploy and as affordable as possible. This paper presents a monitoring system for mid to long-term event recognition based on RGB-D (Red Green Blue + Depth) standard algorithms and on additional algorithms in order to address a real world application. Using a hierarchical modelbased approach, the robustness of this system is evaluated on the recognition of physical tasks (e.g., balance test) undertaken by older people (N = 30) during a clinical protocol devoted to dementia study. The performance of the system is demonstrated at recognizing, first, human postures, and second, complex events based on posture and 3D contextual information of the scene

Comparison of effective regurgitant orifice area by the PISA method and tricuspid coaptation gap measurement to identify very severe tricuspid regurgitation and stratify mortality risk

IntroductionVarious definitions of very severe (VS) tricuspid regurgitation (TR) have been proposed based on the effective regurgitant orifice area (EROA) or tricuspid coaptation gap (TCG). Because of the inherent limitations associated with the EROA, we hypothesized that the TCG would be more suitable for defining VSTR and predicting outcomes.Materials and methodsIn this French multicentre retrospective study, we included 606 patients with ≥moderate-to-severe isolated functional TR (without structural valve disease or an overt cardiac cause) according to the recommendations of the European Association of Cardiovascular Imaging. Patients were further stratified into VSTR according to the EROA (≥60 mm2) and then according to the TCG (≥10 mm). The primary endpoint was all-cause mortality and the secondary endpoint was cardiovascular mortality.ResultsThe relationship between the EROA and TCG was poor (R2 = 0.22), especially when the size of the defect was large. Four-year survival was comparable between patients with an EROA <60 mm2 vs. ≥60 mm2 (68 ± 3% vs. 64 ± 5%, p = 0.89). A TCG ≥10 mm was associated with lower four-year survival than a TCG <10 mm (53 ± 7% vs. 69 ± 3%, p < 0.001). After adjustment for covariates, including comorbidity, symptoms, dose of diuretics, and right ventricular dilatation and dysfunction, a TCG ≥10 mm remained independently associated with higher all-cause mortality (adjusted HR[95% CI] = 1.47[1.13–2.21], p = 0.019) and cardiovascular mortality (adjusted HR[95% CI] = 2.12[1.33–3.25], p = 0.001), whereas an EROA ≥60 mm2 was not associated with all-cause or cardiovascular mortality (adjusted HR[95% CI]: 1.16[0.81–1.64], p = 0.416, and adjusted HR[95% CI]: 1.07[0.68–1.68], p = 0.784, respectively)ConclusionThe correlation between the TCG and EROA is weak and decreases with increasing defect size. A TCG ≥10 mm is associated with increased all-cause and cardiovascular mortality and should be used to define VSTR in isolated significant functional TR

Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe