Pub Date : 2010-10-01DOI: 10.1109/AIPR.2010.5759687
A. Gardezi, Ahmad Alkandri, P. Birch, T. Qureshi, R. Young, C. Chatwin
A space variant Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter is designed specifically for images acquired from a forward looking infrared (FLIR) sensor, using the maximum of the power spectral density (PSD) of the input image instead of the white noise covariance factor. The kernel can be locally modified depending upon its position in the input frame, which enables adaptation of the filter dependant on background heat signature variances and also enables the normalization of the filter energy levels. The detection capabilities of the filter were evaluated using different data sets of real images and 3D models for a suspected threat in order to define a thresholding parameter. The parameter was based on peak to correlation energy (PCE) and peak to side lobe ratio (PSR) of the correlation output which led to the definition of a criterion for predicting true and false detections. The hardware implementation of the system has been discussed in terms of FPGA versus DSP chipsets and a performance benchmark has been created using millions of multiply-accumulate operations per second (MMAC) and the cost. In this paper we propose an implementation and performance evaluation of a security detection application which uses a space variant OT-MACH filter with different data sets. Also a performance benchmark has been created for the hardware implementation of the proposed system on popular FPGA and DSP chipsets.
{"title":"An implementation and performance evaluation of a space variant OT-MACH filter for a security detection application using FLIR sensor","authors":"A. Gardezi, Ahmad Alkandri, P. Birch, T. Qureshi, R. Young, C. Chatwin","doi":"10.1109/AIPR.2010.5759687","DOIUrl":"https://doi.org/10.1109/AIPR.2010.5759687","url":null,"abstract":"A space variant Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter is designed specifically for images acquired from a forward looking infrared (FLIR) sensor, using the maximum of the power spectral density (PSD) of the input image instead of the white noise covariance factor. The kernel can be locally modified depending upon its position in the input frame, which enables adaptation of the filter dependant on background heat signature variances and also enables the normalization of the filter energy levels. The detection capabilities of the filter were evaluated using different data sets of real images and 3D models for a suspected threat in order to define a thresholding parameter. The parameter was based on peak to correlation energy (PCE) and peak to side lobe ratio (PSR) of the correlation output which led to the definition of a criterion for predicting true and false detections. The hardware implementation of the system has been discussed in terms of FPGA versus DSP chipsets and a performance benchmark has been created using millions of multiply-accumulate operations per second (MMAC) and the cost. In this paper we propose an implementation and performance evaluation of a security detection application which uses a space variant OT-MACH filter with different data sets. Also a performance benchmark has been created for the hardware implementation of the proposed system on popular FPGA and DSP chipsets.","PeriodicalId":128378,"journal":{"name":"2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123252285","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 : 2010-10-01DOI: 10.1109/AIPR.2010.5759709
M. Youssef, V. Asari, R. Tompkins, J. Foytik
Activity recognition has been applied to many varied applications ranging from surveillance to medical analysis. Interpreting human actions is often a complex problem for computer vision. Actions can be classified through shape, motion or region based algorithms. While all have their distinct advantages, we consider a feature extraction approach using convexity defects. This algorithmic approach offers a unique method for identifying actions by extracting features from hull convexity defects. Specifically, we create a hull around the segmented silhouette of interest in which the regions that exist in the hull are recognized. A feature database is created through a dataset of features for multiple individuals. These feature points are registered between progressive frames and then normalized for analysis. Using Principal Component Analysis (PCA), the feature points are classified to different poses. From there testing and training is performed to observe the classification into major human activities. This approach offers a robust and accurate method to identify actions and is invariant to size and human shape.
{"title":"Hull convexity defects features for human activity recognition","authors":"M. Youssef, V. Asari, R. Tompkins, J. Foytik","doi":"10.1109/AIPR.2010.5759709","DOIUrl":"https://doi.org/10.1109/AIPR.2010.5759709","url":null,"abstract":"Activity recognition has been applied to many varied applications ranging from surveillance to medical analysis. Interpreting human actions is often a complex problem for computer vision. Actions can be classified through shape, motion or region based algorithms. While all have their distinct advantages, we consider a feature extraction approach using convexity defects. This algorithmic approach offers a unique method for identifying actions by extracting features from hull convexity defects. Specifically, we create a hull around the segmented silhouette of interest in which the regions that exist in the hull are recognized. A feature database is created through a dataset of features for multiple individuals. These feature points are registered between progressive frames and then normalized for analysis. Using Principal Component Analysis (PCA), the feature points are classified to different poses. From there testing and training is performed to observe the classification into major human activities. This approach offers a robust and accurate method to identify actions and is invariant to size and human shape.","PeriodicalId":128378,"journal":{"name":"2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128427400","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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