66,325 research outputs found
PROCESSING VIDEOS USING PARALLEL COMPUTING: A NOVEL APPROACH
In this paper, the proposed framework is presented that supports acquiring high-resolution video’s from the low-resolution. The high-resolution videos could be used in tagging, identifying and tracking people. We concentrate on two aspects. One, data simplification method as the algorithm required for conversion large amount of data processing which is run in parallel. Second, is building a parallel video processing techniques as pipeline for analyzing image modules such as face detection, recognition and tracking so that multiple people can be identified more efficiently and smoothly with increased performance and computational efficiency. Parallel processing techniques makes the use of super resolution algorithm obsolete for major modification in generating high-resolution video images. Recognition of multiple people with super resolution can be tracked from real time live videos or it could be recorded on
Multimodal music information processing and retrieval: survey and future challenges
Towards improving the performance in various music information processing
tasks, recent studies exploit different modalities able to capture diverse
aspects of music. Such modalities include audio recordings, symbolic music
scores, mid-level representations, motion, and gestural data, video recordings,
editorial or cultural tags, lyrics and album cover arts. This paper critically
reviews the various approaches adopted in Music Information Processing and
Retrieval and highlights how multimodal algorithms can help Music Computing
applications. First, we categorize the related literature based on the
application they address. Subsequently, we analyze existing information fusion
approaches, and we conclude with the set of challenges that Music Information
Retrieval and Sound and Music Computing research communities should focus in
the next years
Towards a Scalable Hardware/Software Co-Design Platform for Real-time Pedestrian Tracking Based on a ZYNQ-7000 Device
Currently, most designers face a daunting task to
research different design flows and learn the intricacies of
specific software from various manufacturers in
hardware/software co-design. An urgent need of creating a
scalable hardware/software co-design platform has become a key
strategic element for developing hardware/software integrated
systems. In this paper, we propose a new design flow for building
a scalable co-design platform on FPGA-based system-on-chip.
We employ an integrated approach to implement a histogram
oriented gradients (HOG) and a support vector machine (SVM)
classification on a programmable device for pedestrian tracking.
Not only was hardware resource analysis reported, but the
precision and success rates of pedestrian tracking on nine open
access image data sets are also analysed. Finally, our proposed
design flow can be used for any real-time image processingrelated
products on programmable ZYNQ-based embedded
systems, which benefits from a reduced design time and provide a
scalable solution for embedded image processing products
Understanding Public Evaluation: Quantifying Experimenter Intervention
Public evaluations are popular because some research
questions can only be answered by turning “to the wild.”
Different approaches place experimenters in different roles
during deployment, which has implications for the kinds of
data that can be collected and the potential bias introduced
by the experimenter. This paper expands our understanding
of how experimenter roles impact public evaluations and
provides an empirical basis to consider different evaluation
approaches. We completed an evaluation of a playful
gesture-controlled display – not to understand interaction at
the display but to compare different evaluation approaches.
The conditions placed the experimenter in three roles,
steward observer, overt observer, and covert observer, to
measure the effect of experimenter presence and analyse the
strengths and weaknesses of each approach
Scalable software architecture for on-line multi-camera video processing
In this paper we present a scalable software architecture for on-line multi-camera video processing, that guarantees a good trade off between computational power, scalability and flexibility. The software system is modular and its main blocks are the Processing Units (PUs), and the Central Unit. The Central Unit works as a supervisor of the running PUs and each PU manages the acquisition phase and the processing phase. Furthermore, an approach to easily parallelize the desired processing application has been presented. In this paper, as case study, we apply the proposed software architecture to a multi-camera system in order to efficiently manage multiple 2D object detection modules in a real-time scenario. System performance has been evaluated under different load conditions such as number of cameras and image sizes. The results show that the software architecture scales well with the number of camera and can easily works with different image formats respecting the real time constraints. Moreover, the parallelization approach can be used in order to speed up the processing tasks with a low level of overhea
Accelerated hardware video object segmentation: From foreground detection to connected components labelling
This is the preprint version of the Article - Copyright @ 2010 ElsevierThis paper demonstrates the use of a single-chip FPGA for the segmentation of moving objects in a video sequence. The system maintains highly accurate background models, and integrates the detection of foreground pixels with the labelling of objects using a connected components algorithm. The background models are based on 24-bit RGB values and 8-bit gray scale intensity values. A multimodal background differencing algorithm is presented, using a single FPGA chip and four blocks of RAM. The real-time connected component labelling algorithm, also designed for FPGA implementation, run-length encodes the output of the background subtraction, and performs connected component analysis on this representation. The run-length encoding, together with other parts of the algorithm, is performed in parallel; sequential operations are minimized as the number of run-lengths are typically less than the number of pixels. The two algorithms are pipelined together for maximum efficiency
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