@conference {14192,
	title = {Active scene recognition with vision and language},
	booktitle = {2011 IEEE International Conference on Computer Vision (ICCV)},
	year = {2011},
	month = {2011/11/06/13},
	pages = {810 - 817},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper presents a novel approach to utilizing high level knowledge for the problem of scene recognition in an active vision framework, which we call active scene recognition. In traditional approaches, high level knowledge is used in the post-processing to combine the outputs of the object detectors to achieve better classification performance. In contrast, the proposed approach employs high level knowledge actively by implementing an interaction between a reasoning module and a sensory module (Figure 1). Following this paradigm, we implemented an active scene recognizer and evaluated it with a dataset of 20 scenes and 100+ objects. We also extended it to the analysis of dynamic scenes for activity recognition with attributes. Experiments demonstrate the effectiveness of the active paradigm in introducing attention and additional constraints into the sensing process.},
	keywords = {accuracy, active scene recognition, classification performance, Cognition, Computer vision, Detectors, Equations, high level knowledge utilization, HUMANS, image classification, inference mechanisms, object detectors, Object recognition, reasoning module, sensing process, sensory module, support vector machines, Training},
	isbn = {978-1-4577-1101-5},
	doi = {10.1109/ICCV.2011.6126320},
	author = {Yu,Xiaodong and Ferm{\"u}ller, Cornelia and Ching Lik Teo and Yezhou Yang and Aloimonos, J.}
}
@article {14506,
	title = {Statistical Relational Learning as an Enabling Technology for Data Acquisition and Data Fusion in Heterogeneous Sensor Networks},
	year = {2008},
	month = {2008/06/29/},
	institution = {OFFICE OF RESEARCH ADMINISTRATION AND ADVANCEMENT, UNIVERSITY OF MARYLAND COLLEGE PARK},
	abstract = {Our work has focused on developing new cost sensitive feature acquisition and classification algorithms, mapping these algorithms onto camera networks, and creating a test bed of video data and implemented vision algorithms that we can use to implement these. First, we will describe a new algorithm that we have developed for feature acquisition in Hidden Markov Models (HMMs). This is particularly useful for inference tasks involving video from a single camera, in which the relationship between frames of video can be modeled as a Markov chain. We describe this algorithm in the context of using background subtraction results to identify portions of video that contain a moving object. Next, we will describe new algorithms that apply to general graphical models. These can be tested using existing test sets that are drawn from a range of domains in addition to sensor networks.},
	keywords = {*ALGORITHMS, *CLASSIFICATION, data acquisition, DATA FUSION, Detectors, Feature extraction, HMM(HIDDEN MARKOV MODELS), NETWORKS, NUMERICAL MATHEMATICS, PE611102, RANDOM FIELDS, STATISTICS AND PROBABILITY, TEST SETS, VIDEO SIGNALS},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA500520},
	author = {Jacobs, David W. and Getoor, Lise}
}
@conference {19665,
	title = {Multitarget tracking with split and merged measurements},
	booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {605 - 610 vol. 1},
	abstract = {In many multitarget tracking applications in computer vision, a detection algorithm provides locations of potential targets. Subsequently, the measurements are associated with previously estimated target trajectories in a data association step. The output of the detector is often imperfect and the detection data may include multiple, split measurements from a single target or a single merged measurement from several targets. To address this problem, we introduce a multiple hypothesis tracker for interacting targets that generate split and merged measurements. The tracker is based on an efficient Markov chain Monte Carlo (MCMC) based auxiliary variable particle filter. The particle filter is Rao-Blackwellized such that the continuous target state parameters are estimated analytically, and an MCMC sampler generates samples from the large discrete space of data associations. In addition, we include experimental results in a scenario where we track several interacting targets that generate these split and merged measurements.},
	keywords = {Application software, Computer vision, Detection algorithms, Detectors, filtering theory, Markov chain Monte Carlo based auxiliary variable particle filter, Markov processes, merged measurements, Monte Carlo methods, multiple hypothesis tracker, multitarget tracking, parameter estimation, Particle filters, Particle tracking, Rao-Blackwellized filter, split measurements, target tracking, Trajectory},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@conference {11908,
	title = {Pedestrian classification from moving platforms using cyclic motion pattern},
	booktitle = {IEEE International Conference on Image Processing, 2005. ICIP 2005},
	volume = {2},
	year = {2005},
	month = {2005/09//},
	pages = {II- 854-7 - II- 854-7},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper describes an efficient pedestrian detection system for videos acquired from moving platforms. Given a detected and tracked object as a sequence of images within a bounding box, we describe the periodic signature of its motion pattern using a twin-pendulum model. Then a principle gait angle is extracted in every frame providing gait phase information. By estimating the periodicity from the phase data using a digital phase locked loop (dPLL), we quantify the cyclic pattern of the object, which helps us to continuously classify it as a pedestrian. Past approaches have used shape detectors applied to a single image or classifiers based on human body pixel oscillations, but ours is the first to integrate a global cyclic motion model and periodicity analysis. Novel contributions of this paper include: i) development of a compact shape representation of cyclic motion as a signature for a pedestrian, ii) estimation of gait period via a feedback loop module, and iii) implementation of a fast online pedestrian classification system which operates on videos acquired from moving platforms.},
	keywords = {compact shape representation, cyclic motion pattern, data mining, Detectors, digital phase locked loop, digital phase locked loops, feedback loop module, gait analysis, gait phase information, human body pixel oscillations, HUMANS, image classification, Image motion analysis, image representation, image sequence, Image sequences, Motion detection, Object detection, pedestrian classification, pedestrian detection system, Phase estimation, Phase locked loops, principle gait angle, SHAPE, tracking, Videos},
	isbn = {0-7803-9134-9},
	doi = {10.1109/ICIP.2005.1530190},
	author = {Yang Ran and Qinfen Zheng and Weiss, I. and Davis, Larry S. and Abd-Almageed, Wael and Liang Zhao}
}
@article {18852,
	title = {Integrated Microelectromechanical System (MEMS) Inertial Measurement Unit (IMIMU)},
	year = {2001},
	month = {2001/12//},
	institution = {CARNEGIE-MELLON UNIVERSITY},
	abstract = {Processes, designs and design tools are developed to enable the monolithic integration of arrays of inertial microsensors with electronics. Accelerometers and gyroscopes, fabricated in a single CMOS process, are functional and demonstrate a single chip IMU. Two integrated post CMOS micro-machining processes are demonstrated. Thin-film microstructures are defined from the metal-dielectric stack of a conventional process. In the second process, a back-side silicon etch, followed by front-side DRIE produces bulk silicon microstructures. Accelerometer and gyroscope designs are developed with accompanying low noise electronic circuitry. Noise performance was limited to 1/f circuit noise. The chip output sensibility is set by the interface circuit design. A thermally stabilized accelerometer and circuit design is demonstrated using embedded polysilicon resistors as temperature sensors and heaters in a closed loop. Nested gyroscope topologies are demonstrated with a lateral MEMS accelerometer used as a coriolis acceleration sensor. Modeling and simulation tools that simultaneously consider the electromechanical transducer and the electronic circuit to predict system performance are developed. Electrical, electromechanical and mechanical parasitics required to enable predictive lumped parameter simulation are identified and can be extracted, enabling a designer to confidently estimate design performance prior to fabrication. Generic physics-based fault models for surface-micromachined actuators and sensors are developed that enable effective testing, diagnosis and design for manufacturability.},
	keywords = {*ELECTROMECHANICAL DEVICES, *GYROSCOPES, *INERTIAL MEASUREMENT UNITS, *INERTIAL NAVIGATION, *MICROELECTROMECHANICAL SYSTEMS, ACCELEROMETERS, BULK MATERIALS, CHIPS(ELECTRONICS), CIRCUITS, CLOSED LOOP SYSTEMS, CORIOLIS EFFECT, Detectors, DIELECTRICS, ELECTRICAL AND ELECTRONIC EQUIPMENT, EMBEDDING, METALS, MICROSENSORS., MICROSTRUCTURE, MONOLITHIC STRUCTURES(ELECTRONICS), NAVIGATION AND GUIDANCE, PARASITES, PE63739E, PERFORMANCE(ENGINEERING), POLYSILICONS, RESISTORS, SILICON, TEMPERATURE SENSITIVE ELEMENTS, THERMAL STABILITY, THIN FILMS, tools, Topology, WUAFRLE1170030},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA399565},
	author = {Fedder,Gary K. and Blanton,Shawn and Carley,L. R. and Gupta, Satyandra K. and Koester,David}
}
