49,267 research outputs found
Capturing Evolution Genes for Time Series Data
The modeling of time series is becoming increasingly critical in a wide
variety of applications. Overall, data evolves by following different patterns,
which are generally caused by different user behaviors. Given a time series, we
define the evolution gene to capture the latent user behaviors and to describe
how the behaviors lead to the generation of time series. In particular, we
propose a uniform framework that recognizes different evolution genes of
segments by learning a classifier, and adopt an adversarial generator to
implement the evolution gene by estimating the segments' distribution.
Experimental results based on a synthetic dataset and five real-world datasets
show that our approach can not only achieve a good prediction results (e.g.,
averagely +10.56% in terms of F1), but is also able to provide explanations of
the results.Comment: a preprint version. arXiv admin note: text overlap with
arXiv:1703.10155 by other author
Online real-time crowd behavior detection in video sequences
Automatically detecting events in crowded scenes is a challenging task in Computer Vision. A number of offline approaches have been proposed for solving the problem of crowd behavior detection, however the offline assumption limits their application in real-world video surveillance systems. In this paper, we propose an online and real-time method for detecting events in crowded video sequences. The proposed approach is based on the combination of visual feature extraction and image segmentation and it works without the need of a training phase. A quantitative experimental evaluation has been carried out on multiple publicly available video sequences, containing data from various crowd scenarios and different types of events, to demonstrate the effectiveness of the approach
HUMAN ACTIVITY RECOGNITION IN SMART-HOME ENVIRONMENTS FOR HEALTH-CARE APPLICATIONS
With a growing population of elderly people, the number of subjects at risk of cognitive
disorders is rapidly increasing. Many research groups are studying pervasive solutions to
continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care
costs and supporting the medical diagnosis.
Clinicians are interested in monitoring several behavioral aspects for a wide variety of
applications: early diagnosis, emergency monitoring, assessment of cognitive disorders,
etcetera. Among the several behavioral aspects of interest, anomalous behaviors while
performing activities of daily living (ADLs) are of great importance. Indeed, these anomalies
can be indicators of serious cognitive diseases like Mild Cognitive Impairment. The
recognition of such abnormal behaviors relies on robust and accurate ADLs recognition
systems. Moreover, in order to enable unobtrusive and privacy-aware monitoring,
environmental sensors in charge of unobtrusively capturing the interaction of the subject with
the home infrastructure should be preferred.
This thesis presents several contributions on this topic. The major ones are two novel hybrid
ADLs recognition algorithms. The former is supervised while the latter is unsupervised.
Preliminary results, which still need to be confirmed, show that the recognition rate of the
unsupervised method is comparable to the one obtained by the supervised one, with the
great advantage of not requiring the acquisition of an annotated dataset.
Beyond ADLs recognition, other contributions on smart sensing and anomaly recognition are
presented. Regarding unobtrusive sensing, we propose a machine learning technique to
detect fine-grained manipulations performed by the inhabitant on household objects
instrumented with tiny accelerometer sensors.
Finally, a novel rule-based framework for the recognition of fine-grained abnormal behaviors
is presented. Experimental results on several datasets show the effectiveness of all the
proposed techniques
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
We present a novel procedural framework to generate an arbitrary number of
labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to
design accurate algorithms or training models for crowded scene understanding.
Our overall approach is composed of two components: a procedural simulation
framework for generating crowd movements and behaviors, and a procedural
rendering framework to generate different videos or images. Each video or image
is automatically labeled based on the environment, number of pedestrians,
density, behavior, flow, lighting conditions, viewpoint, noise, etc.
Furthermore, we can increase the realism by combining synthetically-generated
behaviors with real-world background videos. We demonstrate the benefits of
LCrowdV over prior lableled crowd datasets by improving the accuracy of
pedestrian detection and crowd behavior classification algorithms. LCrowdV
would be released on the WWW
Neuronal glucose transporter isoform 3 deficient mice demonstrate features of autism spectrum disorders.
Neuronal glucose transporter (GLUT) isoform 3 deficiency in null heterozygous mice led to abnormal spatial learning and working memory but normal acquisition and retrieval during contextual conditioning, abnormal cognitive flexibility with intact gross motor ability, electroencephalographic seizures, perturbed social behavior with reduced vocalization and stereotypies at low frequency. This phenotypic expression is unique as it combines the neurobehavioral with the epileptiform characteristics of autism spectrum disorders. This clinical presentation occurred despite metabolic adaptations consisting of an increase in microvascular/glial GLUT1, neuronal GLUT8 and monocarboxylate transporter isoform 2 concentrations, with minimal to no change in brain glucose uptake but an increase in lactate uptake. Neuron-specific glucose deficiency has a negative impact on neurodevelopment interfering with functional competence. This is the first description of GLUT3 deficiency that forms a possible novel genetic mechanism for pervasive developmental disorders, such as the neuropsychiatric autism spectrum disorders, requiring further investigation in humans
A system for learning statistical motion patterns
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction
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