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RICCARDO GASPARINI
COLLABORATORE IN SPIN OFF
Dipartimento di Ingegneria "Enzo Ferrari"
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Pubblicazioni
2021
- MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?
[Relazione in Atti di Convegno]
Fabbri, Matteo; Braso, Guillem; Maugeri, Gianluca; Cetintas, Orcun; Gasparini, Riccardo; Osep, Aljosa; Calderara, Simone; Leal-Taixe, Laura; Cucchiara, Rita
abstract
2020
- Anomaly Detection for Vision-based Railway Inspection
[Relazione in Atti di Convegno]
Gasparini, Riccardo; Pini, Stefano; Borghi, Guido; Scaglione, Giuseppe; Calderara, Simone; Fedeli, Eugenio; Cucchiara, Rita
abstract
2020
- Anomaly Detection, Localization and Classification for Railway Inspection
[Relazione in Atti di Convegno]
Gasparini, Riccardo; D'Eusanio, Andrea; Borghi, Guido; Pini, Stefano; Scaglione, Giuseppe; Calderara, Simone; Fedeli, Eugenio; Cucchiara, Rita
abstract
2017
- Embedded Recurrent Network for Head Pose Estimation in Car
[Relazione in Atti di Convegno]
Borghi, Guido; Gasparini, Riccardo; Vezzani, Roberto; Cucchiara, Rita
abstract
An accurate and fast driver's head pose estimation is a rich source of information, in particular in the automotive context. Head pose is a key element for driver's behavior investigation, pose analysis, attention monitoring and also a useful component to improve the efficacy of Human-Car Interaction systems. In this paper, a Recurrent Neural Network is exploited to tackle the problem of driver head pose estimation, directly and only working on depth images to be more reliable in presence of varying or insufficient illumination. Experimental results, obtained from two public dataset, namely Biwi Kinect Head Pose and ICT-3DHP Database, prove the efficacy of the proposed method that overcomes state-of-art works. Besides, the entire system is implemented and tested on two embedded boards with real time performance.
2016
- Optimizing image registration for interactive applications
[Relazione in Atti di Convegno]
Gasparini, Riccardo; Alletto, Stefano; Serra, Giuseppe; Cucchiara, Rita
abstract
With the spread of wearable and mobile devices, the request for interactive augmented reality applications is in constant growth. Among the different possibilities, we focus on the cultural heritage domain where a key step in the development applications for augmented cultural experiences is to obtain a precise localization of the user, i.e. the 6 degree-of-freedom of the camera acquiring the images used by the application. Current state of the art perform this task by extracting local descriptors from a query and exhaustively matching them to a sparse 3D model of the environment. While this procedure obtains good localization performance, due to the vast search space involved in the retrieval of 2D-3D correspondences this is often not feasible in real-time and interactive environments. In this paper we hence propose to perform descriptor quantization to reduce the search space and employ multiple KD-Trees combined with a principal component analysis dimensionality reduction to enable an efficient search. We experimentally show that our solution can halve the computational requirements of the correspondence search with regard to the state of the art while maintaining similar accuracy levels.