In this paper; we propose a novel crowd behavior representation method to detect abnormal behaviors in videos. An adaptive optical flow filtering method is proposed to utilize low-level optical flow informations. Furt...
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In this paper; we propose a novel crowd behavior representation method to detect abnormal behaviors in videos. An adaptive optical flow filtering method is proposed to utilize low-level optical flow informations. Furthermore, a simple framework is developed to detect and to localize abnormal crowd behavior using adaptive optical flow filtering result. The proposed method is more robust than other modeling methods in representing different behaviors. In this model, a normal behavior is presented by the general value. Some outliers in the temporal domain or spatial domain are presented by a higher value. Spatio-temporal cuboids are extracted from the filtering result to present the likelihood of anomaly in the frame. Experimental evaluations are performed on two public datasets with comparison to the provisos abnormal behavior detection methods in the literature. Experimental results show that the proposed methods outperform previous abnormal behavior detection techniques in the literature.
Anesthesia-induced altered arousal depends on drugs having their effect in specific brain regions. These effects are also reflected in autonomic nervous system (ANS) outflow dynamics. To this extent, instantaneous mon...
Anesthesia-induced altered arousal depends on drugs having their effect in specific brain regions. These effects are also reflected in autonomic nervous system (ANS) outflow dynamics. To this extent, instantaneous monitoring of ANS outflow, based on neurophysiological and computational modeling, may provide a more accurate assessment of the action of anesthetic agents on the cardiovascular system. This will aid anesthesia care providers in maintaining homeostatic equilibrium and help to minimize drug administration while maintaining antinociceptive effects. In previous studies, we established a point process paradigm for analyzing heartbeat dynamics and have successfully applied these methods to a wide range of cardiovascular data and protocols. We recently devised a novel instantaneous nonlinear assessment of ANS outflow, also suitable and effective for real-time monitoring of the fast hemodynamic and autonomic effects during induction and emergence from anesthesia. Our goal is to demonstrate that our framework is suitable for instantaneous monitoring of the ANS response during administration of a broad range of anesthetic drugs. Specifically, we compare the hemodynamic and autonomic effects in study participants undergoing propofol (PROP) and dexmedetomidine (DMED) administration. Our methods provide an instantaneous characterization of autonomic state at different stages of sedation and anesthesia by tracking autonomic dynamics at very high time-resolution. Our results suggest that refined methods for analyzing linear and nonlinear heartbeat dynamics during administration of specific anesthetic drugs are able to overcome nonstationary limitations as well as reducing inter-subject variability, thus providing a potential real-time monitoring approach for patients receiving anesthesia.
Video-sharing websites have begun to provide easy access to user-generated video content. How do we find what we want to view among the huge video database? When people search for a video, they may want to know whethe...
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The planning of goal-directed movement towards targets in different parts of space is an important function of the brain. Such visuo-motor planning and execution is known to involve multiple brain regions, including v...
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ISBN:
(纸本)9781424479276
The planning of goal-directed movement towards targets in different parts of space is an important function of the brain. Such visuo-motor planning and execution is known to involve multiple brain regions, including visual, parietal, and frontal cortices. To understand how these brain regions work together to both plan and execute goal-directed movement, it is essential to describe the dynamic causal interactions among them. Here we model causal interactions of distributed cortical source activity derived from non-invasively recorded EEG, using a combination of ICA, minimum-norm distributed source localization (cLORETA), and dynamical modeling within the Source Information Flow Toolbox (SIFT). We differentiate network causal connectivity of reach planning and execution, by comparing the causal network in a speeded reaching task with that for a control task not requiring goal-directed movement. Analysis of a pilot dataset (n=5) shows the utility of this technique and reveals increased connectivity between visual, motor and frontal brain regions during reach planning, together with decreased cross-hemisphere visual coupling during planning and execution, possibly related to task demands.
Robot Operating System, or ROS, is poised to do the same for robots. Morgan Quigley programmed the first iteration of what grew into ROS as a graduate student in 2006, and today his opensource code is redefining the p...
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Robot Operating System, or ROS, is poised to do the same for robots. Morgan Quigley programmed the first iteration of what grew into ROS as a graduate student in 2006, and today his opensource code is redefining the practical limits of robotics. Since version 1.0 was released in 2010, ROS has become the de facto standard in robotics software. Unlike more conventional robotic technology, Quigley's four-fingered hand is not controlled by a central processor. Its fingers and palm distribute computing chores among 14 low-cost, low-power processors dedicated to controlling each joint directly. The masterstroke in Quigley's design is not strictly technical but social. Members of the community who produce a finished release can distribute it themselves, rather than having to house it on central servers.
In this paper, we propose a new method to detect abnormal behavior in crowd video. The motion influence matrix is proposed to represent crowd behaviors. It is generated based on concept of human perception with block-...
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In this paper, we propose a new method to detect abnormal behavior in crowd video. The motion influence matrix is proposed to represent crowd behaviors. It is generated based on concept of human perception with block-level motion vectors which describe actual crowd movement. Furthermore, a generalized framework is developed to detect abnormal crowd behavior using motion influence matrix. The proposed method has an advantage of that does not require any human detection or segmentation method which make it robust to human detection error by using optical flows which is extracted from two continuous frames. In this model, a normal behavior is presented by a low motion influence value. On the other hand, a high motion influence value indicates occurrence of abnormal behavior. Spatio-temporal cuboids are extracted from the motion influence matrix to measure the unusualness of the frame. Two different kinds of abnormal behaviors are dealt in this research: global abnormal behavior and local abnormal behavior. For t quantitative measurement of effectiveness of the proposed method, we evaluate our algorithm on two datasets: UMN and UCSD for global and local abnormal behavior, respectively. Experimental results show that the proposed method outperforms the competing methods.
Facial expressions are one of the most important elements for our social interaction. Automatic processing and recognition of facial expressions is hence one of the core areas in computer vision, computer graphics, an...
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Facial expressions are one of the most important elements for our social interaction. Automatic processing and recognition of facial expressions is hence one of the core areas in computer vision, computer graphics, and social signal processing. Conditional Random Fields (CRFs) and their extensions are widely used for recognizing facial expressions. Most research in this area, however, is done either with a limited set of emotional expressions (such as the six universal expressions), or it concentrates on extracting facial action units (individual muscle movements) from video sequences. Little research has been conducted to analyze the complex facial movements that occur in conversational contexts. Conversational expressions such as "agree", "disagree", "thinking", "looking confused", however, form an integral part of non-verbal communication and systems that can automatically parse and understand such expressions are a key ingredient for the development of efficient human-computer interaction systems. Since conversational expressions may consists of several sub-expressions and contain complex dynamics, however, standard CRF approaches are not suited for the task. In this paper, we conduct a detailed comparison of CRFs and Latent Dynamic Conditional Random Fields (LDCRFs) for recognizing complex conversational expressions. We show the importance of modeling sub-expression dynamics and discuss challenges for applying LDCRFs to recognize a large set of conversational expressions.
Non-invasive brain-computer interfaces (BCIs) allow users to control external devices by their intentions. Nevertheless, most current BCI systems rely on cues or tasks to which the subject has to react (i.e., synchron...
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Non-invasive brain-computer interfaces (BCIs) allow users to control external devices by their intentions. Nevertheless, most current BCI systems rely on cues or tasks to which the subject has to react (i.e., synchronous BCIs). Such systems have limited applications in the real world. It is more desirable for the user to decide himself, when he likes to control a device. However, these so-called asynchronous BCI systems, that rely on electroencephalogram (EEG) measurements show the demand for higher accuracy and stability. Previously, hybrid BCI systems, relying on simultaneous EEG and near-infrared spectroscopy (NIRS) measurements, have been shown to increase the classification performance of (synchronous) motor imagery (MI) tasks. Here we present the first asynchronous hybrid BCI with encouraging results.
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