Tesi etd-03262024-221800 |
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Tipo di tesi
Tesi di laurea magistrale
URN
etd-03262024-221800
Titolo
Quantized Convolutional Neural Networks for Real-Time Vehicle Tracking
Dipartimento
INFORMATICA
Corso di studi
INFORMATICA
Relatori
.
relatore Prof. Bacciu, Davide
relatore Prof. Licitra, Gaetano
relatore Ing. D'Alessandro, Francesco
relatore Prof. Licitra, Gaetano
relatore Ing. D'Alessandro, Francesco
Parole chiave
- convolutional neural network
- noise pollution
- quantization
- real-time
- traffic flow
- vehicle tracking
Data inizio appello
12/04/2024
Consultabilità
Non consultabile
Data di rilascio
12/04/2027
Riassunto (Inglese)
Riassunto (Italiano)
Traffic flow and speed are among the most important parameters needed to be estimated when studying noise pollution. In this thesis, a real-time multiple-vehicle recognition system was developed using a low-cost system consisting of a quantized convolutional network with a tracking algorithm, capable of counting the amount of vehicles passing through and their associated acceleration and speed information.
The results obtained through the proposed methodology provide a low-cost but yet powerful tool for the corresponding authorities involved in the task of noise pollution assessment.
The results obtained through the proposed methodology provide a low-cost but yet powerful tool for the corresponding authorities involved in the task of noise pollution assessment.
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