Firearm Detection and Segmentation Using an Ensemble of Semantic Neural Networks
| Autor: | Alexander Egiazarov, Kamer Vishi, Vasileios Mavroeidis, Fabio Massimo Zennaro |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Vision and Pattern Recognition (cs.CV) False positives and false negatives I.2.6 I.2.10 J.7 Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Convolutional neural network Synthetic data Machine Learning (cs.LG) Crowds Component (UML) 0202 electrical engineering electronic engineering information engineering Segmentation Set (psychology) 0105 earth and related environmental sciences Artificial neural network business.industry 68T01 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | EISIC |
| DOI: | 10.1109/eisic49498.2019.9108871 |
| Popis: | In recent years we have seen an upsurge in terror attacks around the world. Such attacks usually happen in public places with large crowds to cause the most damage possible and get the most attention. Even though surveillance cameras are assumed to be a powerful tool, their effect in preventing crime is far from clear due to either limitation in the ability of humans to vigilantly monitor video surveillance or for the simple reason that they are operating passively. In this paper, we present a weapon detection system based on an ensemble of semantic Convolutional Neural Networks that decomposes the problem of detecting and locating a weapon into a set of smaller problems concerned with the individual component parts of a weapon. This approach has computational and practical advantages: a set of simpler neural networks dedicated to specific tasks requires less computational resources and can be trained in parallel; the overall output of the system given by the aggregation of the outputs of individual networks can be tuned by a user to trade-off false positives and false negatives; finally, according to ensemble theory, the output of the overall system will be robust and reliable even in the presence of weak individual models. We evaluated our system running simulations aimed at assessing the accuracy of individual networks and the whole system. The results on synthetic data and real-world data are promising, and they suggest that our approach may have advantages compared to the monolithic approach based on a single deep convolutional neural network. 8 pages, 8 figures, 2 tables, 2019 European Intelligence and Security Informatics Conference (EISIC) |
| Databáze: | OpenAIRE |
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