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161 results on '"Foresti, G.L."'

1. A Security Assistance System Combining Person Tracking with Chemical Attributes and Video Event Analysis

Author

Becher, Christopher, Foresti, G.L., Kaul, Peter, Koch, W., Lorenz, F.P., Lubczyk, D., Micheloni, C., Piciarelli, C., Safenreiter, K., Siering, C., Varela, M., Waldvogel, S.R., Wieneke, M., Zacharias, Christoph, editor, ter Horst, Klaus W., editor, Witt, Kurt-Ulrich, editor, Sommer, Volker, editor, Ant, Marc, editor, Essmann, Ulrich, editor, and Mülheims, Laurenz, editor

Published
2009
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3. An adaptive high-order neural tree for pattern recognition

Author

Foresti, G.L. and Dolso, T.

Subjects
Neural network, Neural networks -- Research
Abstract

A new neural tree model, called adaptive high-order neural tree (AHNT), is proposed for classifying large sets of multidimensional patterns. The AHNT is built by recursively dividing the training set into subsets and by assigning each subset to a different child node. Each node is composed of a high-order perceptron (HOP) whose order is automatically tuned taking into account the complexity of the pattern set reaching that node. First-order nodes divide the input space with hyperplanes, while HOPs divide the input space arbitrarily, but at the expense of increased complexity. Experimental results demonstrate that the AHNT generalizes better than trees with homogeneous nodes, produces small trees and avoids the use of complex comparative statistical tests and/or a priori selection of large parameter sets. Index Terms--High-order perceptrons (HOPs), learning, neural trees, pattern recognition.

Published
2004

4. A real-time system for video surveillance of unattended outdoor environments

Author

Foresti, G.L.

Subjects
Electronic surveillance -- Research, Image processing -- Research, Motion detectors -- Research, Object recognition (Computers) -- Research, Real-time systems -- Research, Business, Computers, Electronics, Electronics and electrical industries
Abstract

This paper describes a visual surveillance system for remote monitoring of unattended outdoor environments. The system, which works in real time, is able to detect, localize, track, and classify multiple objects moving in a surveilled area. The object classification task is based on a statistical morphological operator, the statistical pecstrum (called specstrum), which is invariant to translations, rotations, and scale variations, and it is robust to noise. Classification is performed by matching the specstrum extracted from each detected object with the specstra extracted from multiple views of different real object models contained in a large database. Outdoor images are used to test the system in real functioning conditions. Performances about good classification percentage, false and missed alarms, viewpoint invariance, noise robustness, and processing time are evaluated. Index Terms - Image processing, object detection, object localization, object tracking, statistical pecstrum, surveillance system.

Published
1998

5. The Eighth Visual Object Tracking VOT2020 Challenge Results

Author

Kristan, M., Leonardis, A., Matas, J., Felsberg, Michael, Pflugfelder, R., Kämäräinen, J.-K., Danelljan, M., Zajc, L.C., Lukežic, A., Drbohlav, O., He, Linbo, Zhang, Yushan, Yan, S., Yang, J., Fernández, G., Hauptmann, A., Memarmoghadam, A., García-Martín, Á., Robinson, Andreas, Varfolomieiev, A., Gebrehiwot, A.H., Uzun, B., Yan, B., Li, B., Qian, C., Tsai, C.-Y., Micheloni, C., Wang, D., Wang, F., Xie, F., Järemo-Lawin, Felix, Gustafsson, F., Foresti, G.L., Bhat, G., Chen, G., Ling, H., Zhang, H., Cevikalp, H., Zhao, H., Bai, H., Kuchibhotla, H.C., Saribas, H., Fan, H., Ghanei-Yakhdan, H., Li, H., Peng, H., Lu, H., Khaghani, J., Bescos, J., Li, J., Fu, J., Yu, J., Xu, J., Kittler, J., Yin, J., Lee, J., Yu, K., Liu, K., Yang, K., Dai, K., Cheng, L., Zhang, L., Wang, L., Van, Gool L., Bertinetto, L., Dunnhofer, M., Cheng, M., Dasari, M.M., Wang, N., Zhang, P., Torr, P.H.S., Wang, Q., Timofte, R., Gorthi, R.K.S., Choi, S., Marvasti-Zadeh, S.M., Zhao, S., Kasaei, S., Qiu, S., Chen, S., Schön, T.B., Xu, T., Lu, W., Hu, W., Zhou, W., Qiu, X., Ke, X., Wu, X.-J., Zhang, X., Yang, X., Zhu, X., Jiang, Y., Wang, Y., Chen, Y., Ye, Y., Li, Y., Yao, Y., Lee, Y., Gu, Y., Wang, Z., Tang, Z., Feng, Z.-H., Mai, Z., Zhang, Z., Wu, Z., Ma, Z., Kristan, M., Leonardis, A., Matas, J., Felsberg, Michael, Pflugfelder, R., Kämäräinen, J.-K., Danelljan, M., Zajc, L.C., Lukežic, A., Drbohlav, O., He, Linbo, Zhang, Yushan, Yan, S., Yang, J., Fernández, G., Hauptmann, A., Memarmoghadam, A., García-Martín, Á., Robinson, Andreas, Varfolomieiev, A., Gebrehiwot, A.H., Uzun, B., Yan, B., Li, B., Qian, C., Tsai, C.-Y., Micheloni, C., Wang, D., Wang, F., Xie, F., Järemo-Lawin, Felix, Gustafsson, F., Foresti, G.L., Bhat, G., Chen, G., Ling, H., Zhang, H., Cevikalp, H., Zhao, H., Bai, H., Kuchibhotla, H.C., Saribas, H., Fan, H., Ghanei-Yakhdan, H., Li, H., Peng, H., Lu, H., Khaghani, J., Bescos, J., Li, J., Fu, J., Yu, J., Xu, J., Kittler, J., Yin, J., Lee, J., Yu, K., Liu, K., Yang, K., Dai, K., Cheng, L., Zhang, L., Wang, L., Van, Gool L., Bertinetto, L., Dunnhofer, M., Cheng, M., Dasari, M.M., Wang, N., Zhang, P., Torr, P.H.S., Wang, Q., Timofte, R., Gorthi, R.K.S., Choi, S., Marvasti-Zadeh, S.M., Zhao, S., Kasaei, S., Qiu, S., Chen, S., Schön, T.B., Xu, T., Lu, W., Hu, W., Zhou, W., Qiu, X., Ke, X., Wu, X.-J., Zhang, X., Yang, X., Zhu, X., Jiang, Y., Wang, Y., Chen, Y., Ye, Y., Li, Y., Yao, Y., Lee, Y., Gu, Y., Wang, Z., Tang, Z., Feng, Z.-H., Mai, Z., Zhang, Z., Wu, Z., and Ma, Z.

Abstract

The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on “real-time” short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net ).

Published
2020
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11. A holographic system for subsea recording and analysis of plankton and other marine particles (HOLOMAR)

Author

Watson, J., Alexander, S., Chalvidan, V., Craig, G., Diard, A., Foresti, G.L., Gentili, S., Hendry, D.C., Hobson, P.R., Lampitt, R.S., Nareid, H., Nebrensky, J.J., Pescetto, A., Pieroni, G.G., Player, M.A., Saw, K., Serpico, S., Tipping, K., Trucco, A., Watson, J., Alexander, S., Chalvidan, V., Craig, G., Diard, A., Foresti, G.L., Gentili, S., Hendry, D.C., Hobson, P.R., Lampitt, R.S., Nareid, H., Nebrensky, J.J., Pescetto, A., Pieroni, G.G., Player, M.A., Saw, K., Serpico, S., Tipping, K., and Trucco, A.

Published
2003

40. A holographic system for subsea recording and analysis of plankton and other marine particles (HOLOMAR)

Author

Watson, J., primary, Alexander, S., additional, Chalvidan, V., additional, Craig, G., additional, Diard, A., additional, Foresti, G.L., additional, Gentili, S., additional, Hendry, D.C., additional, Hobson, P.R., additional, Lampitt, R.S., additional, Nareid, H., additional, Nebrensky, J.J., additional, Pescetto, A., additional, Pieroni, G.G., additional, Player, M.A., additional, Saw, K., additional, Serpico, S., additional, Tipping, K., additional, and Trucco, A., additional

Published
2003
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