Browsing by Author "Minozzo, Laercio"
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- Experiences on object tracking using a many-core embedded systemPublication . Minozzo, Laercio; Rufino, José; Lima, JoséObject localization and tracking is core to many practical applications, like human-computer interaction, security and surveillance, robot competitions and Industry 4.0. Such task may be computationally demanding, especially for traditional embedded systems, that usually have tight processing and storage constraints. This calls for the investigation of alternatives, including emergent heterogeneous embedded systems, like the Parallella line of single-board-computers (SBCs). The work presented in this paper explores the use of a Parallella board with a 16-core Epiphany co-processor, to perform real-time tracking of objects in frames captured by a Kinect sensor, based on color segmentation. We addressed several processing strategies, trying to assess which one performed better. We also ran the same code (where applicable) in several models of the Raspberry Pi platform, for comparison. We conclude that effectively exploring the Epiphany co-processor is not trivial, requiring considerable programming effort and suitable applications (CPU-demanding and highly parallelizable), to the extent that simpler development approaches, on more recent SBCs may be more effective.
- Object tracking using a many-core embedded systemPublication . Minozzo, Laercio; Rufino, José; Lima, José; Menezes, Paulo Lopes de; Cândido Junior, ArnaldoObject localization and tracking is essential for many practical applications, such as mancomputer interaction, security and surveillance, robot competitions, and Industry 4.0. Because of the large amount of data present in an image, and the algorithmic complexity involved, this task can be computationally demanding, mainly for traditional embedded systems, due to their processing and storage limitations. This calls for investigation and experimentation with new approaches, as emergent heterogeneous embedded systems, that promise higher performance, without compromising energy e ciency. This work explores several real-time color-based object tracking techniques, applied to images supplied by a RGB-D sensor attached to di erent embedded platforms. The main motivation was to explore an heterogeneous Parallella board with a 16-core Epiphany coprocessor, to reduce image processing time. Another goal was to confront this platform with more conventional embedded systems, namely the popular Raspberry Pi family. In this regard, several processing options were pursued, from low-level implementations specially tailored to the Parallella, to higher-level multi-platform approaches. The results achieved allow to conclude that the programming e ort required to e - ciently use the Epiphany co-processor is considerable. Also, for the selected case study, the performance attained was bellow the one o ered by simpler approaches running on quad-core Raspberry Pi boards.