The Experts below are selected from a list of 1875 Experts worldwide ranked by ideXlab platform
Corneliu Rusu - One of the best experts on this subject based on the ideXlab platform.
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SpeD - Several classifiers for Intruder Detection applications
2017 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), 2017Co-Authors: Elena Roxana Buhus, Lacrimioara Grama, Corneliu RusuAbstract:The goal of this work is to present some possible Intruder Detection systems and the influence of impulse-like signals upon the overall classification accuracy. Two different scenarios are used: in the first scenario five sound classes are considered (last class belong to impulsive sounds — gunshots), while in the second scenario we dropped out the impulsive sound class. More classifiers are used in both scenarios and different number of features are considered. An improvement in the classification accuracy is obtained within the second scenario. The highest accuracy for the first scenario is for J48 classifier using 51 features, while for the second scenario the highest accuracy is attained for Simple Logistic classifier wit 101 features.
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EUROCAST - The Quantization Effect on Audio Signals for Wildlife Intruder Detection Systems
Computer Aided Systems Theory – EUROCAST 2015, 2015Co-Authors: Lacrimioara Grama, Corneliu RusuAbstract:In this paper we present the influence of quantization of audio signals on D and S descriptors. These descriptors represent the number of samples between two consecutive real zeros and the number of points of local minima/maxima between two successive real zeros, respectively. It is shown that the number of the D/S pairs is almost constant and the behavior is almost the same till the number of bits is less than 6, in the proposed audio based wildlife Intruder Detection framework.
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spectrograms sparsograms and spectral signatures for wildlife Intruder Detection
2015 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), 2015Co-Authors: Corneliu Rusu, Lacrimioara GramaAbstract:In this paper some properties of spectrograms and sparsograms are reviewed. The framework addressed is acoustic based wildlife Intruder Detection. The spectral signatures are also recalled within this framework. The averaged binary sparsogram is introduced and it is shown that it can be considered an effective tool for classifying the possible Intruders sounds into different classes.
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SpeD - Spectrograms, sparsograms and spectral signatures for wildlife Intruder Detection
2015 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), 2015Co-Authors: Corneliu Rusu, Lacrimioara GramaAbstract:In this paper some properties of spectrograms and sparsograms are reviewed. The framework addressed is acoustic based wildlife Intruder Detection. The spectral signatures are also recalled within this framework. The averaged binary sparsogram is introduced and it is shown that it can be considered an effective tool for classifying the possible Intruders sounds into different classes.
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about quantization of audio signals for wildlife Intruder Detection systems
European Conference on Circuit Theory and Design, 2015Co-Authors: Lacrimioara Grama, Corneliu RusuAbstract:In this work we study the quantization of audio signals for a zero-crossing method recently used to detect Intruders in wildlife areas. This method implements two descriptors: D (represents the number of samples between two real zeros) and S (represents the number of points of local minima/maxima between two consecutive real zeros). We show using experimental results that in the proposed audio based wildlife Intruder Detection framework, the number of D/S pairs are almost constant till the number of bits used for quantization is less than six.
Lacrimioara Grama - One of the best experts on this subject based on the ideXlab platform.
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SpeD - Several classifiers for Intruder Detection applications
2017 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), 2017Co-Authors: Elena Roxana Buhus, Lacrimioara Grama, Corneliu RusuAbstract:The goal of this work is to present some possible Intruder Detection systems and the influence of impulse-like signals upon the overall classification accuracy. Two different scenarios are used: in the first scenario five sound classes are considered (last class belong to impulsive sounds — gunshots), while in the second scenario we dropped out the impulsive sound class. More classifiers are used in both scenarios and different number of features are considered. An improvement in the classification accuracy is obtained within the second scenario. The highest accuracy for the first scenario is for J48 classifier using 51 features, while for the second scenario the highest accuracy is attained for Simple Logistic classifier wit 101 features.
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EUROCAST - The Quantization Effect on Audio Signals for Wildlife Intruder Detection Systems
Computer Aided Systems Theory – EUROCAST 2015, 2015Co-Authors: Lacrimioara Grama, Corneliu RusuAbstract:In this paper we present the influence of quantization of audio signals on D and S descriptors. These descriptors represent the number of samples between two consecutive real zeros and the number of points of local minima/maxima between two successive real zeros, respectively. It is shown that the number of the D/S pairs is almost constant and the behavior is almost the same till the number of bits is less than 6, in the proposed audio based wildlife Intruder Detection framework.
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spectrograms sparsograms and spectral signatures for wildlife Intruder Detection
2015 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), 2015Co-Authors: Corneliu Rusu, Lacrimioara GramaAbstract:In this paper some properties of spectrograms and sparsograms are reviewed. The framework addressed is acoustic based wildlife Intruder Detection. The spectral signatures are also recalled within this framework. The averaged binary sparsogram is introduced and it is shown that it can be considered an effective tool for classifying the possible Intruders sounds into different classes.
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SpeD - Spectrograms, sparsograms and spectral signatures for wildlife Intruder Detection
2015 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), 2015Co-Authors: Corneliu Rusu, Lacrimioara GramaAbstract:In this paper some properties of spectrograms and sparsograms are reviewed. The framework addressed is acoustic based wildlife Intruder Detection. The spectral signatures are also recalled within this framework. The averaged binary sparsogram is introduced and it is shown that it can be considered an effective tool for classifying the possible Intruders sounds into different classes.
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about quantization of audio signals for wildlife Intruder Detection systems
European Conference on Circuit Theory and Design, 2015Co-Authors: Lacrimioara Grama, Corneliu RusuAbstract:In this work we study the quantization of audio signals for a zero-crossing method recently used to detect Intruders in wildlife areas. This method implements two descriptors: D (represents the number of samples between two real zeros) and S (represents the number of points of local minima/maxima between two consecutive real zeros). We show using experimental results that in the proposed audio based wildlife Intruder Detection framework, the number of D/S pairs are almost constant till the number of bits used for quantization is less than six.
Christoph Sulzbachner - One of the best experts on this subject based on the ideXlab platform.
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reliable Intruder Detection using combined modalities of intensity thermal infrared and stereo depth
Advanced Video and Signal Based Surveillance, 2015Co-Authors: Andreas Zweng, Csaba Beleznai, Christoph SulzbachnerAbstract:The task of large-area visual monitoring for the protection of critical and public infrastructures calls for reliable automated visual surveillance systems. Reliability in this context implies that a high Detection accuracy of critical events shall be maintained independent from observation conditions, appearance and pose variations of observed objects (persons, cars), while accomplishing a low-rate of false alarms. In this paper we propose a real-time Intruder Detection system based on a combination of multiple complementary sensor modalities. The proposed system employs a trinocular stereo setup consisting of intensity and thermal infrared cameras capable to cover an observation area of 60m deep × 20m wide where modalities of intensity, stereo depth and thermal infrared are measured and combined to detect, track and classify objects entering the observed area. The individual modalities are combined using an estimated 3d ground plane as a common reference, yielding a probabilistic occupancy map for object candidates. A fast non-parametric clustering technique well coping with noise and multiple nearby objects is used to delineate objects, taking scale information, given the ground plane, into account. The proposed system is validated on two challenging video sequences. Promising Intruder Detection results are presented in terms Detection and false alarm rates.
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AVSS - Reliable Intruder Detection using combined modalities of intensity, thermal infrared and stereo depth
2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2015Co-Authors: Andreas Zweng, Csaba Beleznai, Christoph SulzbachnerAbstract:The task of large-area visual monitoring for the protection of critical and public infrastructures calls for reliable automated visual surveillance systems. Reliability in this context implies that a high Detection accuracy of critical events shall be maintained independent from observation conditions, appearance and pose variations of observed objects (persons, cars), while accomplishing a low-rate of false alarms. In this paper we propose a real-time Intruder Detection system based on a combination of multiple complementary sensor modalities. The proposed system employs a trinocular stereo setup consisting of intensity and thermal infrared cameras capable to cover an observation area of 60m deep × 20m wide where modalities of intensity, stereo depth and thermal infrared are measured and combined to detect, track and classify objects entering the observed area. The individual modalities are combined using an estimated 3d ground plane as a common reference, yielding a probabilistic occupancy map for object candidates. A fast non-parametric clustering technique well coping with noise and multiple nearby objects is used to delineate objects, taking scale information, given the ground plane, into account. The proposed system is validated on two challenging video sequences. Promising Intruder Detection results are presented in terms Detection and false alarm rates.
Andreas Zweng - One of the best experts on this subject based on the ideXlab platform.
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reliable Intruder Detection using combined modalities of intensity thermal infrared and stereo depth
Advanced Video and Signal Based Surveillance, 2015Co-Authors: Andreas Zweng, Csaba Beleznai, Christoph SulzbachnerAbstract:The task of large-area visual monitoring for the protection of critical and public infrastructures calls for reliable automated visual surveillance systems. Reliability in this context implies that a high Detection accuracy of critical events shall be maintained independent from observation conditions, appearance and pose variations of observed objects (persons, cars), while accomplishing a low-rate of false alarms. In this paper we propose a real-time Intruder Detection system based on a combination of multiple complementary sensor modalities. The proposed system employs a trinocular stereo setup consisting of intensity and thermal infrared cameras capable to cover an observation area of 60m deep × 20m wide where modalities of intensity, stereo depth and thermal infrared are measured and combined to detect, track and classify objects entering the observed area. The individual modalities are combined using an estimated 3d ground plane as a common reference, yielding a probabilistic occupancy map for object candidates. A fast non-parametric clustering technique well coping with noise and multiple nearby objects is used to delineate objects, taking scale information, given the ground plane, into account. The proposed system is validated on two challenging video sequences. Promising Intruder Detection results are presented in terms Detection and false alarm rates.
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AVSS - Reliable Intruder Detection using combined modalities of intensity, thermal infrared and stereo depth
2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2015Co-Authors: Andreas Zweng, Csaba Beleznai, Christoph SulzbachnerAbstract:The task of large-area visual monitoring for the protection of critical and public infrastructures calls for reliable automated visual surveillance systems. Reliability in this context implies that a high Detection accuracy of critical events shall be maintained independent from observation conditions, appearance and pose variations of observed objects (persons, cars), while accomplishing a low-rate of false alarms. In this paper we propose a real-time Intruder Detection system based on a combination of multiple complementary sensor modalities. The proposed system employs a trinocular stereo setup consisting of intensity and thermal infrared cameras capable to cover an observation area of 60m deep × 20m wide where modalities of intensity, stereo depth and thermal infrared are measured and combined to detect, track and classify objects entering the observed area. The individual modalities are combined using an estimated 3d ground plane as a common reference, yielding a probabilistic occupancy map for object candidates. A fast non-parametric clustering technique well coping with noise and multiple nearby objects is used to delineate objects, taking scale information, given the ground plane, into account. The proposed system is validated on two challenging video sequences. Promising Intruder Detection results are presented in terms Detection and false alarm rates.
Deepak Khosla - One of the best experts on this subject based on the ideXlab platform.
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fuzzy edge symmetry features for improved Intruder Detection
IEEE International Conference on Fuzzy Systems, 2003Co-Authors: Narayan Srinivasa, Swarup Medasani, Yuri Owechko, Deepak KhoslaAbstract:The paper proposes a new set of fuzzy features based on symmetry of edges for improving the accuracy of detecting Intruders. We show that the proposed fuzzy edge-symmetry feature-based classifier is comparable to the Detection accuracy of a multi-scale wavelet feature system for Intruder Detection. We also present two approaches to fusing the results of classifiers trained independently on the edge-symmetry and wavelet features. Experimental results clearly indicate the improvement in system performance when the results of the two classifiers are fused.
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FUZZ-IEEE - Fuzzy edge-symmetry features for improved Intruder Detection
The 12th IEEE International Conference on Fuzzy Systems 2003. FUZZ '03., 2003Co-Authors: Narayan Srinivasa, Swarup Medasani, Yuri Owechko, Deepak KhoslaAbstract:The paper proposes a new set of fuzzy features based on symmetry of edges for improving the accuracy of detecting Intruders. We show that the proposed fuzzy edge-symmetry feature-based classifier is comparable to the Detection accuracy of a multi-scale wavelet feature system for Intruder Detection. We also present two approaches to fusing the results of classifiers trained independently on the edge-symmetry and wavelet features. Experimental results clearly indicate the improvement in system performance when the results of the two classifiers are fused.
