Prof. Giovanni Pau Profile

Prof. Giovanni Pau

at Univ. degli Studi di Bologna

SPIE Involvement:

Author

Publications (4)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 12 October 2022 Paper

Enabling deep reinforcement learning autonomous driving by 3D-LiDAR point clouds

Yuhan Chen, Rita Tse, Michael Bosello, Davide Aguiari, Su-Kit Tang, Giovanni Pau

Proceedings Volume 12342, 1234218 (2022) https://doi.org/10.1117/12.2644369

KEYWORDS: LIDAR, Data modeling, Sensors, Environmental sensing, Machine learning, Convolution, Computer simulations, Detection and tracking algorithms

Read Abstract +

Autonomous driving holds the promise of revolutionizing our lives and society. Robot drivers will run errands such as commuting, parking cars, or taking kids to school. It is expected that, by the mid-century, humans will drive only for their pleasure. Autonomous vehicles will increase the efficiency and safety of the transportation system by reducing accidents and increasing the overall system capacity. Current autonomous driving systems are based on supervised learning that relies on massive, labeled data. It takes a lot of time, resources, and manpower to produce such data sets. While this approach is achieving remarkable results, the required effort to produce data becomes a limiting factor for general driving scenarios. This research explores Reinforcement Learning to advance autonomous driving models without labeled data. Reinforcement Learning is a learning paradigm that uses the concept of rewards to autonomously discover, through trial & error, how to solve a task. This work uses the LiDAR sensor as a case study to explore the effectiveness of Reinforcement Learning in interpreting complex data. LiDARs provide a dynamic high time-space definition map of the environment and it could be one of the key sensors for autonomous driving.

Proceedings Article | 12 October 2022 Paper

Tobacco plant disease dataset

Hong Lin, Rita Tse, Su-Kit Tang, Zhenping Qiang, Jinliang Ou, Giovanni Pau

Proceedings Volume 12342, 123423Q (2022) https://doi.org/10.1117/12.2644288

KEYWORDS: Image segmentation, Pathogens, Image classification, Pathology, Computer science, Agriculture, Visual process modeling, Target detection

Read Abstract +

Tobacco is a valuable plant in agricultural and commercial industry. Any disease infection to the plant may lower the harvest and interfere the operation of supply chain in the market. Image-based deep learning methods are cutting-edge technologies that can facilitate the diagnosis of diseases efficiently and effectively when large-scale dataset is available for training. However, there is not a public dataset about tobacco currently. A comprehensive dataset is appealed to take advantage of deep learning methods in tobacco cultivation urgently. In this paper, we propose to create a specific dataset for tobacco diseases, called Tobacco Plant Disease Dataset (TPDD). 2721 tobacco leaf images are taken in field. The dataset serves for two purposes: disease classification and leaf detection. For classification, we identify 12 classes and provide two types of disease annotations: 1) Whole Leaf Section; 2) Disease Fragment Section. For leaf detection, we provide two kinds of bounding box: rectangle bounding box and polygon bounding box. In addition, we conduct baseline experiments to illustrate the usefulness of TPDD: 1) using deep learning model to detect single disease and multiple diseases; 2) using YOLO-v3 and Mask-RCNN to detect leaves. We hope that the dataset could support the tobacco industry, also be a benchmark in fine-grained vision classification.

Proceedings Article | 30 June 2021 Paper

ReSPEcT: privacy respecting thermal-based specific person recognition

Ngai Seng Chan, Ka Ian Chan, Rita Tse, Su-Kit Tang, Giovanni Pau

Proceedings Volume 11878, 1187802 (2021) https://doi.org/10.1117/12.2599271

Read Abstract +

Video analytic techniques have been used to extract high level information from video streams. The technique leverages advances on machine learning to summarize complex image data into simple alert-signal to attract the attention of human operators. For example, in a station for the underground video analytic can help the operator to focus on an event from a specific camera rather than leaving this only to the human eye. A concern of such techniques is privacy as they expose people identity and enable profiling of personal habits and orientations. This work introduces ReSPEcT (Privacy Respecting theRmal basEd Specific Person rECogniTion), a privacy preserving video analytic system based on thermal video streams. ReSPEcT is able to identify a specific-human in thermal video streams from low-cost, low resolution cameras. The system leverages recent advances in machine learning (CNNs) and a plethora of pre-processing mechanisms, such as image automatic labeling, image segmentation, and image augmentation to reduce the stream background noise, improve resilience, strengthen human-body classification, and finally enable a specific human-target identification. ReSPEcT’s automatic labeling tool significantly reduces time thus automatically performing labeling using a model that can be retrained by an interactive web application. The experimental evaluation shows that overall ReSPEcT achieve 96.83% accuracy in identifying a specific person. Furthermore, is important to notice that while ReSPEcT can identify a specific human, the tool is not aware of the realidentity as it operates only on thermal images. ReSPEcT paves the way to use video analytic in a variety of privacyprotected scenarios, such as confidential meetings, sensitive spaces, or even public toilets.

Proceedings Article | 12 June 2020 Paper

DeepClass: edge based class occupancy detection aided by deep learning and image cropping

Rita Tse, Lorenzo Monti, Marcus Im, Silvia Mirri, Giovanni Pau, Paola Salomoni

Proceedings Volume 11519, 1151904 (2020) https://doi.org/10.1117/12.2572948

KEYWORDS: Cameras, Sensors, Image processing, Image analysis, Prototyping, Data modeling, Environmental sensing, Detection and tracking algorithms, Light sources and illumination, Internet

Read Abstract +

Detecting people's presence, monitoring their flows, and their activities, counting how many persons are in a specific place can be strategic goals in different contexts, providing useful insights for different purposes, including those ones related to the management of staying quality in indoor environments. In particular, having information about the actual and current occupancy of a specific room, in specific hours, could be strategic in providing interesting and helpful information for smart building management. In fact, this information could be needed to adequately set the Heat, Ventilation and Air Conditioning (HVAC), the alarm, the lighting systems, and other management issues also. In this context, the Internet of Things paradigm, together with the diffusion of the availability of sensors and smart objects, can provide significant support in monitoring and detecting daily life activities in various situations. Moreover, advancements and specific analysis in image processing can play a strategic role in guaranteeing and improving accuracy, whenever cameras are involved in these situations, to get pictures from the monitored environments. In this paper, we present a people counting approach we have defined and adopted to monitor persons' presence in smart campus classrooms, which is based on the use of cameras and Raspberry Pi platforms. Such an approach has been improved thanks to specific image processing strategies, to be generalized and adopted in different indoor environments, without the need for a specific training phase. The paper presents some evaluation tests we have conducted, showing the accuracy of our approach.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years