Clustering Optimization for Abnormality Detection in Semi-Autonomous Systems-Reference-Cited by-同舟云学术

Clustering Optimization for Abnormality Detection in Semi-Autonomous Systems

Published:2019 Issue: Volume: Page:
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Container-title:1st International Workshop on Multimodal Understanding and Learning for Embodied Applications - MULEA '19
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Author:

Iqbal Hafsa¹,Campo Damian¹,Baydoun Mohamad¹,Marcenaro Lucio¹,Gomez David Martin²,Regazzoni Carlo¹

Affiliation:

1. University of Genoa, Genoa, Italy

2. University of Carlos III Madrid, Madrid, Spain

Publisher

ACM Press

Reference22 articles.

1. Norhashim Mohd Arshad and Noorfadzli Abdul Razak. 2012. Vision-based detection technique for effective line-tracking autonomus vehicle. In 2012 IEEE 8th International Colloquium on Signal Processing and its Applications. IEEE, 441--445.

2. M Baydoun, D Campo, V Sanguineti, Lucio Marcenaro, A Cavallaro, and C Regazzoni. 2018. Learning switching models for abnormality detection for autonomous driving. In 2018 21st International Conference on Information Fusion (FUSION). IEEE, 2606--2613.

3. George A Bekey. 2005. Autonomous robots: from biological inspiration to implementation and control. MIT press.

4. D Campo, M Baydoun, L Marcenaro, A Cavallaro, and CS Regazzoni. 2018. Unsupervised Trajectory Modeling Based on Discrete Descriptors for Classifying Moving Objects in Video Sequences. In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 833--837.

5. Donato Di Paola, Annalisa Milella, Grazia Cicirelli, and Arcangelo Distante. 2010. An autonomous mobile robotic system for surveillance of indoor environments. International Journal of Advanced Robotic Systems 7, 1 (2010), 8.

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