1,660 research outputs found
VideoWall Bench: A Benchmark for Evaluating Hardware Accelerated Video Decoding on Linux
VideoWall Bench is a benchmark script for benchmarking video decoding capabilities using hardware acceleration on Linux. Intel has introduced Video Acceleration API (VA-API) which enabled and provides access for graphics hardware to do hardware acceleration. VA API provides a set of video decoders (Codecs) for the H.264 video standards. Multiple video decoding using video wall methodology is a method of benchmarking that be implemented in this script. Using this method, users can really stress the multiple video decoding capabilities of one platform and at the same time measure processor usage for video decoding process. VideoWall Bench benchmark video decoding performance by measuring processor utilization, memory utilization, total frame rate per second (FPS) and time fluctuation in video decoding process. Additionally, VideoWall Bench also includes set
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We propose a spatiotemporal architecture for
anomaly detection in videos including crowded scenes. Our architecture includes
two main components, one for spatial feature representation, and one for
learning the temporal evolution of the spatial features. Experimental results
on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of
our method is comparable to state-of-the-art methods at a considerable speed of
up to 140 fps
Enhancing a Neurosurgical Imaging System with a PC-based Video Processing Solution
This work presents a PC-based prototype video processing application developed to be used with a specific neurosurgical imaging device, the OPMI® PenteroTM operating microscope, in the Department of Neurosurgery of Helsinki University Central Hospital at Töölö, Helsinki. The motivation for implementing the software was the lack of some clinically important features in the imaging system provided by the microscope.
The imaging system is used as an online diagnostic aid during surgery. The microscope has two internal video cameras; one for regular white light imaging and one for near-infrared fluorescence imaging, used for indocyanine green videoangiography. The footage of the microscope’s current imaging mode is accessed via the composite auxiliary output of the device. The microscope also has an external high resolution white light video camera, accessed via a composite output of a separate video hub.
The PC was chosen as the video processing platform for its unparalleled combination of prototyping and high-throughput video processing capabilities. A thorough analysis of the platform and efficient video processing methods was conducted in the thesis and the results were used in the design of the imaging station. The features found feasible during the project were incorporated into a video processing application running on a GNU/Linux distribution Ubuntu. The clinical usefulness of the implemented features was ensured beforehand by consulting the neurosurgeons using the original system.
The most significant shortcomings of the original imaging system were mended in this work. The key features of the developed application include: live streaming, simultaneous streaming and recording, and playing back of upto two video streams. The playback mode provides full media player controls, with a frame-by-frame precision rewinding, in an intuitive and responsive interface. A single view and a side-by-side comparison mode are provided for the streams. The former gives more detail, while the latter can be used, for example, for before-after and anatomic-angiographic comparisons.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format
Physical Representation-based Predicate Optimization for a Visual Analytics Database
Querying the content of images, video, and other non-textual data sources
requires expensive content extraction methods. Modern extraction techniques are
based on deep convolutional neural networks (CNNs) and can classify objects
within images with astounding accuracy. Unfortunately, these methods are slow:
processing a single image can take about 10 milliseconds on modern GPU-based
hardware. As massive video libraries become ubiquitous, running a content-based
query over millions of video frames is prohibitive.
One promising approach to reduce the runtime cost of queries of visual
content is to use a hierarchical model, such as a cascade, where simple cases
are handled by an inexpensive classifier. Prior work has sought to design
cascades that optimize the computational cost of inference by, for example,
using smaller CNNs. However, we observe that there are critical factors besides
the inference time that dramatically impact the overall query time. Notably, by
treating the physical representation of the input image as part of our query
optimization---that is, by including image transforms, such as resolution
scaling or color-depth reduction, within the cascade---we can optimize data
handling costs and enable drastically more efficient classifier cascades.
In this paper, we propose Tahoma, which generates and evaluates many
potential classifier cascades that jointly optimize the CNN architecture and
input data representation. Our experiments on a subset of ImageNet show that
Tahoma's input transformations speed up cascades by up to 35 times. We also
find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy,
and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In
Proceedings of the 35th IEEE International Conference on Data Engineering
(ICDE 2019
A new adaptive colorization filter for video decompression
HD content is more in demand and requires a lot of bandwidth. In this paper, a new real-time adaptive colorization filter for HD videos is presented. This approach reduces the required bandwidth by reducing non-key frames in the HD video sequence to grayscale and colourizing these frames at the decompression stage. Additionally this technique determines the frame status based on the image information
Inverse tone mapping
The introduction of High Dynamic Range Imaging in computer graphics has produced a novelty
in Imaging that can be compared to the introduction of colour photography or even more.
Light can now be captured, stored, processed, and finally visualised without losing information.
Moreover, new applications that can exploit physical values of the light have been introduced
such as re-lighting of synthetic/real objects, or enhanced visualisation of scenes. However,
these new processing and visualisation techniques cannot be applied to movies and pictures
that have been produced by photography and cinematography in more than one hundred years.
This thesis introduces a general framework for expanding legacy content into High Dynamic
Range content. The expansion is achieved avoiding artefacts, producing images suitable for
visualisation and re-lighting of synthetic/real objects. Moreover, it is presented a methodology
based on psychophysical experiments and computational metrics to measure performances of
expansion algorithms. Finally, a compression scheme, inspired by the framework, for High
Dynamic Range Textures, is proposed and evaluated
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