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PAOLO SANTINELLI

Docente a contratto
Dipartimento di Scienze e Metodi dell'Ingegneria

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Pubblicazioni

2015 - A General-Purpose Sensing Floor Architecture for Human-Environment Interaction [Articolo su rivista]
Vezzani, Roberto; Lombardi, Martino; Pieracci, Augusto; Santinelli, Paolo; Cucchiara, Rita
abstract

Smart environments are now designed as natural interfaces to capture and understand human behavior without a need for explicit human-computer interaction. In this paper, we present a general-purpose architecture that acquires and understands human behaviors through a sensing floor. The pressure field generated by moving people is captured and analyzed. Specific actions and events are then detected by a low-level processing engine and sent to high-level interfaces providing different functions. The proposed architecture and sensors are modular, general-purpose, cheap, and suitable for both small- and large-area coverage. Some sample entertainment and virtual reality applications that we developed to test the platform are presented.

2014 - Substrate for a sensitive floor and method for displaying loads on the substrate [Brevetto]
Lucchese, Claudio; Cucchiara, Rita; Lombardi, Martino; Pieracci, Augusto; Santinelli, Paolo; Vezzani, Roberto
abstract

The substrate (1; 50) for making a sensitive floor comprises: a first frame made of high-conductivity sensing means (2a-2d) having a first orientation; a second frame made of high-conductivity sensing means (3a-3d) which is adapted to be laid on said first frame and has a second orientation, other than said first orientation, said second frame (3a-3d) forming a support layer for floor finishing products; an element (4) made of a conductive material, which comprises: an elastically compressible thickness (S1), two opposite faces (104, 204) contacting said two first and second frames (2a-2d), (3a-3d), an electric resistor whose resistance is proportional to said thickness (S1).

2013 - Human Behavior Understanding with Wide Area Sensing Floors [Relazione in Atti di Convegno]
Lombardi, Martino; Pieracci, Augusto; Santinelli, Paolo; Vezzani, Roberto; Cucchiara, Rita
abstract

The research on innovative and natural interfaces aims at developing devices able to capture and understand the human behavior without the need of a direct interaction. In this paper we propose and describe a framework based on a sensing floor device. The pressure field generated by people or objects standing on the floor is captured and analyzed. Local and global features are computed by a low level processing unit and sent to high level interfaces. The framework can be used in different applications, such as entertainment, education or surveillance. A detailed description of the sensing element and the processing architectures is provided, together with some sample applications developed to test the device capabilities.

2013 - Sensing floors for privacy-compliant surveillance of wide areas [Relazione in Atti di Convegno]
Lombardi, Martino; Pieracci, Augusto; Santinelli, Paolo; Vezzani, Roberto; Cucchiara, Rita
abstract

Surveillance systems can really benefit from the integration of multiple and heterogeneous sensors. In this paper we describe an innovative sensing floor. Thanks to its low cost and ease of installation, the floor is suitable for both private and public environments, from narrow zones to wide areas. The floor is made adding a sensing layer below commercial floating tiles. The sensor is scalable, reliable, and completely invisible to the users. The temporal and spatial resolutions of the data are high enough to identify the presence of people, to recognize their behavior and to detect events in a privacy compliant way. Experimental results on a real prototype implementation confirm the potentiality of the framework.

2012 - Veiling Luminance estimation on FPGA-based embedded smart camera [Relazione in Atti di Convegno]
Grana, Costantino; Borghesani, Daniele; Santinelli, Paolo; Cucchiara, Rita
abstract

This paper describes the design and development of a Veiling Luminance estimation system based on the use of a CMOS image sensor, fully implemented on FPGA. The system is composed of the CMOS Image sensor, FPGA, DDR SDRAM, USB controller and SPI (Serial Peripheral Interface) Flash. The FPGA is used to build a system-on-chip integrating a soft processor (Xilinx MicroBlaze) and all the hardware blocks needed to handle the external peripherals and memory. The soft processor is used to handle image acquisition and all computational tasks need to compute the Veiling Luminance value. The advantages of this single chip FPGA implementation include the reduction of the hardware requirements, power consumption, and system complexity. The problem of the high dynamic range images have been addressed with multiple acquisitions at different exposure times. Vignetting, radial distortion and angular weighting, as required by veiling luminance definition, are handled by a single integer look-up table (LUT) access. Results are compared with a state of the art certified instrument.

2011 - Energy-efficient Feedback Tracking on Embedded Smart Cameras by Hardware-level Optimization [Relazione in Atti di Convegno]
M., Casares; Santinelli, Paolo; S., Velipasalar; Prati, Andrea; Cucchiara, Rita
abstract

Embedded systems have limited processing power, memory and energy. When camera sensors are added to an embedded system, the problem of limited resources becomes even more pronounced. In this paper, we introduce two methodologies to increase the energy-efficiency and battery-life of an embeddedsmart camera by hardware-level operations when performingobject detection and tracking. The CITRIC platform is employedas our embedded smart camera. First, down-sampling is performed at hardware level on the micro-controller of the imagesensor rather than performing software-level down-sampling atthe main microprocessor of the camera board. In addition, instead of performing object detection and tracking on wholeimage, we first estimate the location of the target in the nextframe, form a search region around it, then crop the next frameby using the HREF and VSYNC signals at the micro-controllerof the image sensor, and perform detection and tracking onlyin the cropped search region. Thus, the amount of data thatis moved from the image sensor to the main memory at eachframe is optimized. Also, we can adaptively change the size ofthe cropped window during tracking depending on the objectsize. Reducing the amount of transferred data, better use ofthe memory resources, and delegating image down-samplingand cropping tasks to the micro-controller on the image sensor,result in significant decrease in energy consumption and increasein battery-life. Experimental results show that hardware-leveldown-sampling and cropping, and performing detection andtracking in cropped regions provide 41.24% decrease in energyconsumption, and 107.2% increase in battery-life. Compared toperforming software-level down-sampling and processing wholeframes, proposed methodology provides an additional 8 hours ofcontinuous processing on 4 AA batteries, increasing the lifetimeof the camera to 15.5 hours.

2011 - Energy-efficient Object Detection and Tracking on Embedded Smart Cameras by Hardware-level Operations at the Image Sensor [Relazione in Atti di Convegno]
M., Casares; Santinelli, Paolo; S., Velipasalar; Prati, Andrea; Cucchiara, Rita
abstract

Embedded smart cameras have limited processing power, memory and energy. In this paper, we introduce two methodologies to increase the energy-efficiency and the battery-life of an embedded smart camera by hardware-level operations when performing object detection and tracking. We use the CITRIC platform as our embedded smart camera. We first perform down-sampling at hardware-level on the microcontroller of the image sensor rather than performing software-level down-sampling at the main microprocessor of the camera board. In addition, instead of performing object detection on whole image, we first estimate the location of the target in the next frame, form a search region around it, then crop the next frame by using the HREF and VSYNC signals at the microcontrollerof the image sensor, and perform detection and tracking only in the cropped search region. Thus, the amount of data that is moved from the image sensor to the main memory at each frame, is greatly reduced. Thanks to reduced data transfer, better use of the memory resources and not occupying the main microprocessor with image down-sampling and cropping tasks, we obtain significant savings in energy consumption and battery-life. Experimental results show that hardware-level down-sampling and cropping, and performing detection in cropped regions provide 54:14% decrease in energy consumption, and 121:25% increase in battery-life compared to performing software-level downsampling and processing whole frame.

2011 - Energy-efficient foreground object detection on embedded smart cameras by hardware-level operations [Relazione in Atti di Convegno]
Casares, M.; Santinelli, P.; Velipasalar, S.; Prati, A.; Cucchiara, R.
abstract

Embedded smart cameras have limited processing power, memory and energy. In this paper, we introduce two methodologies to increase the energy-efficiency and the battery-life of an embedded smart camera by hardware-level operations when performing foreground object detection. We use the CITRIC platform as our embedded smart camera. We first perform down-sampling at hardware level on the micro-controller of the image sensor rather than performing software-level down-sampling at the main microprocessor of the camera board. In addition, we crop an image frame at hardware level by using the HREF and VSYNC signals at the micro-controller of the image sensor to perform foreground object detection only in the cropped search region instead of the whole image. Thus, the amount of data that is moved from the image sensor to the main memory at each frame, is greatly reduced. Thanks to reduced data transfer, better use of the memory resources and not occupying the main microprocessor with image down-sampling and cropping tasks, we obtain significant savings in energy consumption and battery-life. Experimental results show that hardware-level down-sampling and cropping, and performing detection in cropped regions provide 54.14% decrease in energy consumption, and 121.25% increase in battery-life compared to performing software-level down-sampling and processing whole frames. © 2011 IEEE.

2010 - High Performance Connected Components Labeling on FPGA [Relazione in Atti di Convegno]
Grana, Costantino; Borghesani, Daniele; Santinelli, Paolo; Cucchiara, Rita
abstract

This paper proposes a comparison of the two most advanced algorithms for connected components labeling, highlighting how they perform on a soft core SoC architecture based on FPGA. In particular we test our block based connected components labeling algorithm, optimized with decision tables and decision trees. The embedded system is composed of the CMOS image sensor, FPGA, DDR SDRAM, USB controller and SPI Flash. Results highlight the importance of caching and instructions and data cache sizes for high performance image processing tasks.

2010 - Mutual Calibration of Camera Motes and RFIDs for People Localization and Identification [Relazione in Atti di Convegno]
Cucchiara, Rita; Fornaciari, Michele; Prati, Andrea; Santinelli, Paolo
abstract

Achieving both localization and identication of people ina wide open area using only cameras can be a challengingtask, which requires cross-cutting requirements : high reso-lution for identication, whereas low resolution for having awide coverage of the localization. Consequently, this paperproposes the joint use of cameras (only devoted to local-ization) and RFID sensors (devoted to identication) withthe nal objective of detecting and localizing intruders. Toground the observations on a common coordinate system,a calibration procedure is dened. This procedure only de-mands a training phase with a single person moving in thescene holding a RFID tag. Although preliminary, the resultsdemonstrate that this calibration is sufficiently accurate tobe applied whenever dierent scenarios, where area of over-lap between the eld of view (FoV) of a camera and theField of sense" (FoS) of a (blind) sensor must be efficientlydetermined.