Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425329
C. Minor, Kevin J. Johnson, S. Rose-Pehrsson, J. Owrutsky, S. Wales, D. Steinhurst, D. Gottuk
The U.S. Naval Research Laboratory has developed an affordable, multisensory, real-time detection system for damage control and situational awareness, called "volume sensor." The system provides standoff identification of events within a space (e.g. flaming and smoldering fires, pipe ruptures, and gas releases) for U.S. Navy vessels. A data fusion approach was used to integrate spectral sensors, acoustic sensors, and video image detection algorithms. Bayesian-based decision algorithms improved event detection rates while reducing false positives. Full scale testing demonstrated that the prototype Volume Sensor performed as well or better than commercial video image detection and point-detection systems in critical quality metrics for fire detection while also providing additional situational awareness. The design framework developed for volume sensor can serve as a template for the integration of heterogeneous sensors into networks for a variety of real-time sensing and situational awareness applications.
{"title":"Data fusion with a multisensor system for damage control and situational awareness","authors":"C. Minor, Kevin J. Johnson, S. Rose-Pehrsson, J. Owrutsky, S. Wales, D. Steinhurst, D. Gottuk","doi":"10.1109/AVSS.2007.4425329","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425329","url":null,"abstract":"The U.S. Naval Research Laboratory has developed an affordable, multisensory, real-time detection system for damage control and situational awareness, called \"volume sensor.\" The system provides standoff identification of events within a space (e.g. flaming and smoldering fires, pipe ruptures, and gas releases) for U.S. Navy vessels. A data fusion approach was used to integrate spectral sensors, acoustic sensors, and video image detection algorithms. Bayesian-based decision algorithms improved event detection rates while reducing false positives. Full scale testing demonstrated that the prototype Volume Sensor performed as well or better than commercial video image detection and point-detection systems in critical quality metrics for fire detection while also providing additional situational awareness. The design framework developed for volume sensor can serve as a template for the integration of heterogeneous sensors into networks for a variety of real-time sensing and situational awareness applications.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133916888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425298
Tsuhan Chen, A. Bimbo, F. Pernici, G. Serra
A calibration algorithm of two cameras using observations of a moving person is presented. Similar methods have been proposed for self-calibration with a single camera, but internal parameter estimation is only limited to the focal length. Recently it has been demonstrated that principal point supposed in the center of the image causes inaccuracy of all estimated parameters. Our method exploits two cameras, using image points of head and foot locations of a moving person, to determine for both cameras the focal length and the principal point. Moreover with the increasing number of cameras there is a demand of procedures to determine their relative placements. In this paper we also describe a method to find the relative position and orientation of two cameras: the rotation matrix and the translation vector which describe the rigid motion between the coordinate frames fixed in two cameras. Results in synthetic and real scenes are presented to evaluate the performance of the proposed method.
{"title":"Accurate self-calibration of two cameras by observations of a moving person on a ground plane","authors":"Tsuhan Chen, A. Bimbo, F. Pernici, G. Serra","doi":"10.1109/AVSS.2007.4425298","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425298","url":null,"abstract":"A calibration algorithm of two cameras using observations of a moving person is presented. Similar methods have been proposed for self-calibration with a single camera, but internal parameter estimation is only limited to the focal length. Recently it has been demonstrated that principal point supposed in the center of the image causes inaccuracy of all estimated parameters. Our method exploits two cameras, using image points of head and foot locations of a moving person, to determine for both cameras the focal length and the principal point. Moreover with the increasing number of cameras there is a demand of procedures to determine their relative placements. In this paper we also describe a method to find the relative position and orientation of two cameras: the rotation matrix and the translation vector which describe the rigid motion between the coordinate frames fixed in two cameras. Results in synthetic and real scenes are presented to evaluate the performance of the proposed method.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114346972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425322
Medha Bhargava, Chia-Chih Chen, M. Ryoo, J. Aggarwal
With concerns about terrorism and global security on the rise, it has become vital to have in place efficient threat detection systems that can detect and recognize potentially dangerous situations, and alert the authorities to take appropriate action. Of particular significance is the case of unattended objects in mass transit areas. This paper describes a general framework that recognizes the event of someone leaving a piece of baggage unattended in forbidden areas. Our approach involves the recognition of four sub-events that characterize the activity of interest. When an unaccompanied bag is detected, the system analyzes its history to determine its most likely owner(s), where the owner is defined as the person who brought the bag into the scene before leaving it unattended. Through subsequent frames, the system keeps a lookout for the owner, whose presence in or disappearance from the scene defines the status of the bag, and decides the appropriate course of action. The system was successfully tested on the i-LIDS dataset.
{"title":"Detection of abandoned objects in crowded environments","authors":"Medha Bhargava, Chia-Chih Chen, M. Ryoo, J. Aggarwal","doi":"10.1109/AVSS.2007.4425322","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425322","url":null,"abstract":"With concerns about terrorism and global security on the rise, it has become vital to have in place efficient threat detection systems that can detect and recognize potentially dangerous situations, and alert the authorities to take appropriate action. Of particular significance is the case of unattended objects in mass transit areas. This paper describes a general framework that recognizes the event of someone leaving a piece of baggage unattended in forbidden areas. Our approach involves the recognition of four sub-events that characterize the activity of interest. When an unaccompanied bag is detected, the system analyzes its history to determine its most likely owner(s), where the owner is defined as the person who brought the bag into the scene before leaving it unattended. Through subsequent frames, the system keeps a lookout for the owner, whose presence in or disappearance from the scene defines the status of the bag, and decides the appropriate course of action. The system was successfully tested on the i-LIDS dataset.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114746523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425282
L. Granai, M. Hamouz, J. Tena, T. Vlachos
This paper proposes a novel 3D lossy compression algorithm tailored for 3D faces. We analyse the effects of compression on the face verification rate and measure recognition performances on the face recognition grand challenge database. Whilst preserving the spatial resolution enabling reconstruction of surface details, the proposed scheme achieves substantial compression to the extent that personal 3D biometric data could fit on a 2D barcode.
{"title":"Compression for 3D face recognition applications","authors":"L. Granai, M. Hamouz, J. Tena, T. Vlachos","doi":"10.1109/AVSS.2007.4425282","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425282","url":null,"abstract":"This paper proposes a novel 3D lossy compression algorithm tailored for 3D faces. We analyse the effects of compression on the face verification rate and measure recognition performances on the face recognition grand challenge database. Whilst preserving the spatial resolution enabling reconstruction of surface details, the proposed scheme achieves substantial compression to the extent that personal 3D biometric data could fit on a 2D barcode.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129102611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425340
Mohammed Saaidia, A. Chaari, S. Lelandais, V. Vigneron, M. Bedda
Face localization using neural network is presented in this communication. Neural network was trained with two different kinds of feature parameters vectors; Zernike moments and eigenfaces. In each case, coordinate vectors of pixels surrounding faces in images were used as target vectors on the supervised training procedure. Thus, trained neural network provides on its output layer a coordinate's vector (rho,thetas) representing pixels surrounding the face contained in treated image. This way to proceed gives accurate faces contours which are well adapted to their shapes. Performances obtained for the two kinds of training feature parameters were recorded using a quantitative measurement criterion according to experiments carried out on the XM2VTS database.
{"title":"Face localization by neural networks trained with Zernike moments and Eigenfaces feature vectors. A comparison","authors":"Mohammed Saaidia, A. Chaari, S. Lelandais, V. Vigneron, M. Bedda","doi":"10.1109/AVSS.2007.4425340","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425340","url":null,"abstract":"Face localization using neural network is presented in this communication. Neural network was trained with two different kinds of feature parameters vectors; Zernike moments and eigenfaces. In each case, coordinate vectors of pixels surrounding faces in images were used as target vectors on the supervised training procedure. Thus, trained neural network provides on its output layer a coordinate's vector (rho,thetas) representing pixels surrounding the face contained in treated image. This way to proceed gives accurate faces contours which are well adapted to their shapes. Performances obtained for the two kinds of training feature parameters were recorded using a quantitative measurement criterion according to experiments carried out on the XM2VTS database.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115318670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-05DOI: 10.1109/AVSS.2007.4425323
Juan C. Alonso-Bayal, R. Santiago-Mozos, J. Leiva-Murillo, M. Lázaro, Antonio Artés-Rodríguez
We implement a fixed-point real-time identification system and provide tools for the optimal design of exponential lookup-tables. This intelligent surveillance system is based on infrared image processing, which allows to detect and track people and trigger different actions depending on the region of the monitored area in which they are. The system automatically segments the body to get the face and includes a face classifier based on the support vector machine.
{"title":"Real-time tracking and identification on an intelligent IR-based surveillance system","authors":"Juan C. Alonso-Bayal, R. Santiago-Mozos, J. Leiva-Murillo, M. Lázaro, Antonio Artés-Rodríguez","doi":"10.1109/AVSS.2007.4425323","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425323","url":null,"abstract":"We implement a fixed-point real-time identification system and provide tools for the optimal design of exponential lookup-tables. This intelligent surveillance system is based on infrared image processing, which allows to detect and track people and trigger different actions depending on the region of the monitored area in which they are. The system automatically segments the body to get the face and includes a face classifier based on the support vector machine.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114897328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-01DOI: 10.1109/AVSS.2007.4425313
Andrew D. Bagdanov, A. Bimbo, F. Dini, W. Nunziati
In particle filter-based visual trackers, dynamic velocity components are typically incorporated into the state update equations. In these cases, there is a risk that the uncertainty in the model update stage can become amplified in unexpected and undesirable ways, leading to erroneous behavior of the tracker. Moreover, the use of a weak appearance model can make the estimates provided by the particle filter inaccurate. To deal with this problem, we propose a continuously adaptive approach to estimating uncertainty in the particle filter, one that balances the uncertainty in its static and dynamic elements. We provide quantitative performance evaluation of the resulting particle filter tracker on a set of ten video sequences. Results are reported in terms of a metric that can be used to objectively evaluate the performance of visual trackers. This metric is used to compare our modified particle filter tracker and the continuously adaptive mean shift tracker. Results show that the performance of the particle filter is significantly improved through adaptive parameter estimation, particularly in cases of occlusion and erratic, nonlinear target motion.
{"title":"Improving the robustness of particle filter-based visual trackers using online parameter adaptation","authors":"Andrew D. Bagdanov, A. Bimbo, F. Dini, W. Nunziati","doi":"10.1109/AVSS.2007.4425313","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425313","url":null,"abstract":"In particle filter-based visual trackers, dynamic velocity components are typically incorporated into the state update equations. In these cases, there is a risk that the uncertainty in the model update stage can become amplified in unexpected and undesirable ways, leading to erroneous behavior of the tracker. Moreover, the use of a weak appearance model can make the estimates provided by the particle filter inaccurate. To deal with this problem, we propose a continuously adaptive approach to estimating uncertainty in the particle filter, one that balances the uncertainty in its static and dynamic elements. We provide quantitative performance evaluation of the resulting particle filter tracker on a set of ten video sequences. Results are reported in terms of a metric that can be used to objectively evaluate the performance of visual trackers. This metric is used to compare our modified particle filter tracker and the continuously adaptive mean shift tracker. Results show that the performance of the particle filter is significantly improved through adaptive parameter estimation, particularly in cases of occlusion and erratic, nonlinear target motion.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122213569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-01DOI: 10.1109/AVSS.2007.4425299
P. Kuo, Jean-Christophe Nebel, D. Makris
This paper presents a novel auto-calibration method from unconstrained human body motion. It relies on the underlying biomechanical constraints associated with human bipedal locomotion. By analysing positions of key points during a sequence, our technique is able to detect frames where the human body adopts a particular posture which ensures the coplanarity of those key points and therefore allows a successful camera calibration. Our technique includes a 3D model adaptation phase which removes the requirement for a precise geometrical 3D description of those points. Our method is validated using a variety of human bipedal motions and camera configurations.
{"title":"Camera auto-calibration from articulated motion","authors":"P. Kuo, Jean-Christophe Nebel, D. Makris","doi":"10.1109/AVSS.2007.4425299","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425299","url":null,"abstract":"This paper presents a novel auto-calibration method from unconstrained human body motion. It relies on the underlying biomechanical constraints associated with human bipedal locomotion. By analysing positions of key points during a sequence, our technique is able to detect frames where the human body adopts a particular posture which ensures the coplanarity of those key points and therefore allows a successful camera calibration. Our technique includes a 3D model adaptation phase which removes the requirement for a precise geometrical 3D description of those points. Our method is validated using a variety of human bipedal motions and camera configurations.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134106088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-01DOI: 10.1109/AVSS.2007.4425335
Eduardo Monari, Charlotte Pasqual
Detection of moving objects is a fundamental task in video based surveillance and security applications. Many detection systems use background estimation methods to model the observed environment. In outdoor surveillance, moving backgrounds (waving trees, clutter) and illumination changes (weather changes, reflections, etc.) are the major challenges for background modelling and the development of a single model that fulfils all these requirements is usually not possible. In this paper we present a background estimation technique for motion detection in non-static backgrounds that overcomes this problem. We introduce an enhanced background estimation architecture with a long-term model and a short-term model. Our system showed that fusion of the detections of these two complementary approaches, improves the quality and reliability of the detection results.
{"title":"Fusion of background estimation approaches for motion detection in non-static backgrounds","authors":"Eduardo Monari, Charlotte Pasqual","doi":"10.1109/AVSS.2007.4425335","DOIUrl":"https://doi.org/10.1109/AVSS.2007.4425335","url":null,"abstract":"Detection of moving objects is a fundamental task in video based surveillance and security applications. Many detection systems use background estimation methods to model the observed environment. In outdoor surveillance, moving backgrounds (waving trees, clutter) and illumination changes (weather changes, reflections, etc.) are the major challenges for background modelling and the development of a single model that fulfils all these requirements is usually not possible. In this paper we present a background estimation technique for motion detection in non-static backgrounds that overcomes this problem. We introduce an enhanced background estimation architecture with a long-term model and a short-term model. Our system showed that fusion of the detections of these two complementary approaches, improves the quality and reliability of the detection results.","PeriodicalId":371050,"journal":{"name":"2007 IEEE Conference on Advanced Video and Signal Based Surveillance","volume":"380 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126278535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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