The Experts below are selected from a list of 9630 Experts worldwide ranked by ideXlab platform
Randy Cole - One of the best experts on this subject based on the ideXlab platform.
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sound source localization for Video Surveillance Camera
Advanced Video and Signal Based Surveillance, 2013Co-Authors: Jacek Stachurski, Lorin P Netsch, Randy ColeAbstract:While Video analytics used in Surveillance applications performs well in normal conditions, it may not work as accurately under adverse circumstances. Taking advantage of the complementary aspects of Video and audio can lead to a more effective analytics framework resulting in increased system robustness. For example, sound scene analysis may indicate potential security risks outside field-of-view, pointing the Camera in that direction. This paper presents a robust low-complexity method for two-microphone estimation of sound direction. While the source localization problem has been studied extensively, a reliable low-complexity solution remains elusive. The proposed direction estimation is based on the Generalized Cross-Correlation with Phase Transform (GCC-PHAT) method. The novel aspects of our approach include band-selective processing and inter-frame filtering of the GCC-PHAT objective function prior to peak detection. The audio bandwidth, microphone spacing, angle resolution, processing delay and complexity can all be adjusted depending on the application requirements. The described algorithm can be used in a multi-microphone configuration for spatial sound localization by combining estimates from microphone pairs. It has been implemented as a real-time demo on a modified TI DM8127 IP Camera. The default 16 kHz audio sampling frequency requires about 5 MIPS processing power in our fixed-point implementation. The test results show robust sound direction estimation under a variety of background noise conditions.
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AVSS - Sound source localization for Video Surveillance Camera
2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2013Co-Authors: Jacek Stachurski, Lorin P Netsch, Randy ColeAbstract:While Video analytics used in Surveillance applications performs well in normal conditions, it may not work as accurately under adverse circumstances. Taking advantage of the complementary aspects of Video and audio can lead to a more effective analytics framework resulting in increased system robustness. For example, sound scene analysis may indicate potential security risks outside field-of-view, pointing the Camera in that direction. This paper presents a robust low-complexity method for two-microphone estimation of sound direction. While the source localization problem has been studied extensively, a reliable low-complexity solution remains elusive. The proposed direction estimation is based on the Generalized Cross-Correlation with Phase Transform (GCC-PHAT) method. The novel aspects of our approach include band-selective processing and inter-frame filtering of the GCC-PHAT objective function prior to peak detection. The audio bandwidth, microphone spacing, angle resolution, processing delay and complexity can all be adjusted depending on the application requirements. The described algorithm can be used in a multi-microphone configuration for spatial sound localization by combining estimates from microphone pairs. It has been implemented as a real-time demo on a modified TI DM8127 IP Camera. The default 16 kHz audio sampling frequency requires about 5 MIPS processing power in our fixed-point implementation. The test results show robust sound direction estimation under a variety of background noise conditions.
Jacek Stachurski - One of the best experts on this subject based on the ideXlab platform.
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sound source localization for Video Surveillance Camera
Advanced Video and Signal Based Surveillance, 2013Co-Authors: Jacek Stachurski, Lorin P Netsch, Randy ColeAbstract:While Video analytics used in Surveillance applications performs well in normal conditions, it may not work as accurately under adverse circumstances. Taking advantage of the complementary aspects of Video and audio can lead to a more effective analytics framework resulting in increased system robustness. For example, sound scene analysis may indicate potential security risks outside field-of-view, pointing the Camera in that direction. This paper presents a robust low-complexity method for two-microphone estimation of sound direction. While the source localization problem has been studied extensively, a reliable low-complexity solution remains elusive. The proposed direction estimation is based on the Generalized Cross-Correlation with Phase Transform (GCC-PHAT) method. The novel aspects of our approach include band-selective processing and inter-frame filtering of the GCC-PHAT objective function prior to peak detection. The audio bandwidth, microphone spacing, angle resolution, processing delay and complexity can all be adjusted depending on the application requirements. The described algorithm can be used in a multi-microphone configuration for spatial sound localization by combining estimates from microphone pairs. It has been implemented as a real-time demo on a modified TI DM8127 IP Camera. The default 16 kHz audio sampling frequency requires about 5 MIPS processing power in our fixed-point implementation. The test results show robust sound direction estimation under a variety of background noise conditions.
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AVSS - Sound source localization for Video Surveillance Camera
2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2013Co-Authors: Jacek Stachurski, Lorin P Netsch, Randy ColeAbstract:While Video analytics used in Surveillance applications performs well in normal conditions, it may not work as accurately under adverse circumstances. Taking advantage of the complementary aspects of Video and audio can lead to a more effective analytics framework resulting in increased system robustness. For example, sound scene analysis may indicate potential security risks outside field-of-view, pointing the Camera in that direction. This paper presents a robust low-complexity method for two-microphone estimation of sound direction. While the source localization problem has been studied extensively, a reliable low-complexity solution remains elusive. The proposed direction estimation is based on the Generalized Cross-Correlation with Phase Transform (GCC-PHAT) method. The novel aspects of our approach include band-selective processing and inter-frame filtering of the GCC-PHAT objective function prior to peak detection. The audio bandwidth, microphone spacing, angle resolution, processing delay and complexity can all be adjusted depending on the application requirements. The described algorithm can be used in a multi-microphone configuration for spatial sound localization by combining estimates from microphone pairs. It has been implemented as a real-time demo on a modified TI DM8127 IP Camera. The default 16 kHz audio sampling frequency requires about 5 MIPS processing power in our fixed-point implementation. The test results show robust sound direction estimation under a variety of background noise conditions.
Sung Bum Pan - One of the best experts on this subject based on the ideXlab platform.
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SSIRI 2009 Fast Abstract Implementation of the Privacy Protection in Video Surveillance System
2009Co-Authors: Hae-min Moon, Sung Bum PanAbstract:Due to increased terrors and crimes, the use of the Video Surveillance Camera system is increasing. It has been operated for public interest such as prevention of crimes and fly-tipping by the police and local government, but private information such as faces or behavior patterns can be recorded in CCTV. When the recorded Video data is exposed, it may cause an invasion to privacy and crimes. This paper analyses conventional methods of privacy protection in Surveillance Camera systems and applied scrambling and RFID system to existing Surveillance systems to prevent privacy exposure in monitoring simultaneously for both privacy protection and Surveillance. The proposed system adjusts the intensities of privacy according to access levels to reduce invasion of privacy by people who are not concerned.
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SSIRI - Implementation of the Privacy Protection in Video Surveillance System
2009 Third IEEE International Conference on Secure Software Integration and Reliability Improvement, 2009Co-Authors: Hae-min Moon, Sung Bum PanAbstract:Due to increased terrors and crimes, the use of the Video Surveillance Camera system is increasing. It has been operated for public interest such as prevention of crimes and fly-tipping by the police and local government, but private information such as faces or behavior patterns can be recorded in CCTV. When the recorded Video data is exposed, it may cause an invasion to privacy and crimes. This paper analyses conventional methods of privacy protection in Surveillance Camera systems and applied scrambling and RFID system to existing Surveillance systems to prevent privacy exposure in monitoring simultaneously for both privacy protection and Surveillance. The proposed system adjusts the intensities of privacy according to access levels to reduce invasion of privacy by people who are not concerned.
Lorin P Netsch - One of the best experts on this subject based on the ideXlab platform.
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sound source localization for Video Surveillance Camera
Advanced Video and Signal Based Surveillance, 2013Co-Authors: Jacek Stachurski, Lorin P Netsch, Randy ColeAbstract:While Video analytics used in Surveillance applications performs well in normal conditions, it may not work as accurately under adverse circumstances. Taking advantage of the complementary aspects of Video and audio can lead to a more effective analytics framework resulting in increased system robustness. For example, sound scene analysis may indicate potential security risks outside field-of-view, pointing the Camera in that direction. This paper presents a robust low-complexity method for two-microphone estimation of sound direction. While the source localization problem has been studied extensively, a reliable low-complexity solution remains elusive. The proposed direction estimation is based on the Generalized Cross-Correlation with Phase Transform (GCC-PHAT) method. The novel aspects of our approach include band-selective processing and inter-frame filtering of the GCC-PHAT objective function prior to peak detection. The audio bandwidth, microphone spacing, angle resolution, processing delay and complexity can all be adjusted depending on the application requirements. The described algorithm can be used in a multi-microphone configuration for spatial sound localization by combining estimates from microphone pairs. It has been implemented as a real-time demo on a modified TI DM8127 IP Camera. The default 16 kHz audio sampling frequency requires about 5 MIPS processing power in our fixed-point implementation. The test results show robust sound direction estimation under a variety of background noise conditions.
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AVSS - Sound source localization for Video Surveillance Camera
2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2013Co-Authors: Jacek Stachurski, Lorin P Netsch, Randy ColeAbstract:While Video analytics used in Surveillance applications performs well in normal conditions, it may not work as accurately under adverse circumstances. Taking advantage of the complementary aspects of Video and audio can lead to a more effective analytics framework resulting in increased system robustness. For example, sound scene analysis may indicate potential security risks outside field-of-view, pointing the Camera in that direction. This paper presents a robust low-complexity method for two-microphone estimation of sound direction. While the source localization problem has been studied extensively, a reliable low-complexity solution remains elusive. The proposed direction estimation is based on the Generalized Cross-Correlation with Phase Transform (GCC-PHAT) method. The novel aspects of our approach include band-selective processing and inter-frame filtering of the GCC-PHAT objective function prior to peak detection. The audio bandwidth, microphone spacing, angle resolution, processing delay and complexity can all be adjusted depending on the application requirements. The described algorithm can be used in a multi-microphone configuration for spatial sound localization by combining estimates from microphone pairs. It has been implemented as a real-time demo on a modified TI DM8127 IP Camera. The default 16 kHz audio sampling frequency requires about 5 MIPS processing power in our fixed-point implementation. The test results show robust sound direction estimation under a variety of background noise conditions.
Xavier Sevillano - One of the best experts on this subject based on the ideXlab platform.
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quickspot a Video analytics solution for on street vacant parking spot detection
Multimedia Tools and Applications, 2016Co-Authors: E. Marmol, Xavier SevillanoAbstract:Vehicles searching for a vacant parking spot on the street can amount to as much as 40 % of the traffic in certain city areas, thus largely affecting mobility in urban environments. For this reason, it would be desirable to create integrated smart traffic management systems capable of providing real-time information to drivers about the location of available vacant parking spots. A scalable solution would consist in exploiting the existing and widely-deployed Video Surveillance Camera networks, which requires the development of computer vision algorithms for detecting vacant parking spots. Following this idea, this work introduces QuickSpot, a car-driven Video analytics solution for on-street vacant parking spot detection designed as a motion detection, object tracking and visual recognition pipeline. One of the main features of QuickSpot is its simplified setup, as it can be trained on external databases to learn the appearances of the objects it is capable of recognizing (pedestrians and vehicles). To test its performance under different daytime lighting conditions, we have recorded, edited, annotated and made available to the research community the QuickSpotDB Video database for the vacant parking spot detection problem. In the conducted experiments, we have evaluated the trade-off between the accuracy and the computational complexity of QuickSpot with an eye to its practical applicability. The results show that QuickSpot detects parking spot status with an average accuracy close to 99 % at a 1-second rate regardless of the illumination conditions, outperforming in an indirect comparison the other car-driven approaches reported in the literature.
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Towards smart traffic management systems: Vacant on-street parking spot detection based on Video analytics
Information Fusion (FUSION), 2014 17th International Conference on, 2014Co-Authors: Xavier Sevillano, E. Marmol, V. Fernandez-ArguedasAbstract:Smart Cities rely on the use of ICTs for a more efficient and intelligent use of resources, whilst improving citizens' quality of life and reducing the environmental footprint. As far as the livability of cities is concerned, traffic is one of the most frequent and complex factors directly affecting citizens. Particularly, drivers in search of a vacant parking spot are a non-negligible source of atmospheric and acoustic pollution. Although some cities have installed sensor-based vacant parking spot detectors in some neighbourhoods, the cost of this approach makes it unfeasible at large scale. As an approach to implement a sustainable solution to the vacant parking spot detection problem in urban environments, this work advocates fusing the information from small-scale sensor-based detectors with that obtained from exploiting the widely-deployed Video Surveillance Camera networks. In particular, this paper focuses on how Video analytics can be exploited as a prior step towards Smart City solutions based on data fusion. Through a set of experiments carefully planned to replicate a real-world scenario, the vacant parking spot detection success rate of the proposed system is evaluated through a critical comparison of local and global visual features (either alone or fused at feature level) and different classifier systems applied to the task. Furthermore, the system is tested under setup scenarios of different complexities, and experimental results show that while local features are best when training with small amounts of highly accurate on-site data, they are outperformed by their global counterparts when training with more samples from an external vehicle database.
