9,687 research outputs found
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
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
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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State-of-the-art on research and applications of machine learning in the building life cycle
Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science
Robust tracking of respiratory rate in high-dynamic range scenes using mobile thermal imaging
The ability to monitor respiratory rate is extremely important for medical
treatment, healthcare and fitness sectors. In many situations, mobile methods,
which allow users to undertake every day activities, are required. However,
current monitoring systems can be obtrusive, requiring users to wear
respiration belts or nasal probes. Recent advances in thermographic systems
have shrunk their size, weight and cost, to the point where it is possible to
create smart-phone based respiration rate monitoring devices that are not
affected by lighting conditions. However, mobile thermal imaging is challenged
in scenes with high thermal dynamic ranges. This challenge is further amplified
by general problems such as motion artifacts and low spatial resolution,
leading to unreliable breathing signals. In this paper, we propose a novel and
robust approach for respiration tracking which compensates for the negative
effects of variations in the ambient temperature and motion artifacts and can
accurately extract breathing rates in highly dynamic thermal scenes. It has
three main contributions. The first is a novel Optimal Quantization technique
which adaptively constructs a color mapping of absolute temperature to improve
segmentation, classification and tracking. The second is the Thermal Gradient
Flow method that computes thermal gradient magnitude maps to enhance accuracy
of the nostril region tracking. Finally, we introduce the Thermal Voxel method
to increase the reliability of the captured respiration signals compared to the
traditional averaging method. We demonstrate the extreme robustness of our
system to track the nostril-region and measure the respiratory rate in high
dynamic range scenes.Comment: Vol. 8, No. 10, 1 Oct 2017, Biomedical Optics Express 4480 - Full
abstract can be found in this journal article (due to limited word counts of
'arXiv abstract'
Urban Spine: A Pedestrian-oriented Multi-modal Transportation Infrastructure for Improving Health and Well-being in the Urban Environment
With finite land resources and ever increasing population, urbanization continues to edge natural environments off our maps. The quality of life and well-being is deteriorated with continuous exposure to the urban environment due to the heavy saturation of stress and anxiety that comes with urban living. Stress is associated with the inherent flight-or-fight reaction that humans have developed through evolution in the natural environment. The contamination of stress inducing stimuli in the urban environment has driving people into sedentary lifestyles, remain indoors within the safe confines of building. Mitigating the magnitude of stressful interactions in the urban landscape, many which are caused by automobiles, will encourage a return to the outdoor environment. The re-integration of naturalistic experiences into the environment will improve the quality of urban life. A shift of the urban landscape toward a pedestrian-orientation, through the promotion of walkability, can ameliorate the adverse impacts caused by automobile centric behavior and cultivate the streetscape as a canvas for experiencing naturalistic features and characteristics that support the health and well-being of the urban dweller – not only ensuring survival but granting the opportunity to flourish
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