53,349 research outputs found
Can you tell a face from a HEVC bitstream?
Image and video analytics are being increasingly used on a massive scale. Not
only is the amount of data growing, but the complexity of the data processing
pipelines is also increasing, thereby exacerbating the problem. It is becoming
increasingly important to save computational resources wherever possible. We
focus on one of the poster problems of visual analytics -- face detection --
and approach the issue of reducing the computation by asking: Is it possible to
detect a face without full image reconstruction from the High Efficiency Video
Coding (HEVC) bitstream? We demonstrate that this is indeed possible, with
accuracy comparable to conventional face detection, by training a Convolutional
Neural Network on the output of the HEVC entropy decoder
Uncertainty-aware video visual analytics of tracked moving objects
Vast amounts of video data render manual video analysis useless while recent automatic video analytics techniques suffer from insufficient performance. To alleviate these issues we present a scalable and reliable approach exploiting the visual analytics methodology. This involves the user in the iterative process of exploration hypotheses generation and their verification. Scalability is achieved by interactive filter definitions on trajectory features extracted by the automatic computer vision stage. We establish the interface between user and machine adopting the VideoPerpetuoGram (VPG) for visualization and enable users to provide filter-based relevance feedback. Additionally users are supported in deriving hypotheses by context-sensitive statistical graphics. To allow for reliable decision making we gather uncertainties introduced by the computer vision step communicate these information to users through uncertainty visualization and grant fuzzy hypothesis formulation to interact with the machine. Finally we demonstrate the effectiveness of our approach by the video analysis mini challenge which was part of the IEEE Symposium on Visual Analytics Science and Technology 2009
Semantic web technologies for video surveillance metadata
Video surveillance systems are growing in size and complexity. Such systems typically consist of integrated modules of different vendors to cope with the increasing demands on network and storage capacity, intelligent video analytics, picture quality, and enhanced visual interfaces. Within a surveillance system, relevant information (like technical details on the video sequences, or analysis results of the monitored environment) is described using metadata standards. However, different modules typically use different standards, resulting in metadata interoperability problems. In this paper, we introduce the application of Semantic Web Technologies to overcome such problems. We present a semantic, layered metadata model and integrate it within a video surveillance system. Besides dealing with the metadata interoperability problem, the advantages of using Semantic Web Technologies and the inherent rule support are shown. A practical use case scenario is presented to illustrate the benefits of our novel approach
DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning
We present DRLViz, a visual analytics interface to interpret the internal
memory of an agent (e.g. a robot) trained using deep reinforcement learning.
This memory is composed of large temporal vectors updated when the agent moves
in an environment and is not trivial to understand due to the number of
dimensions, dependencies to past vectors, spatial/temporal correlations, and
co-correlation between dimensions. It is often referred to as a black box as
only inputs (images) and outputs (actions) are intelligible for humans. Using
DRLViz, experts are assisted to interpret decisions using memory reduction
interactions, and to investigate the role of parts of the memory when errors
have been made (e.g. wrong direction). We report on DRLViz applied in the
context of video games simulators (ViZDoom) for a navigation scenario with item
gathering tasks. We also report on experts evaluation using DRLViz, and
applicability of DRLViz to other scenarios and navigation problems beyond
simulation games, as well as its contribution to black box models
interpretability and explainability in the field of visual analytics
Two-Stream Action Recognition-Oriented Video Super-Resolution
We study the video super-resolution (SR) problem for facilitating video
analytics tasks, e.g. action recognition, instead of for visual quality. The
popular action recognition methods based on convolutional networks, exemplified
by two-stream networks, are not directly applicable on video of low spatial
resolution. This can be remedied by performing video SR prior to recognition,
which motivates us to improve the SR procedure for recognition accuracy.
Tailored for two-stream action recognition networks, we propose two video SR
methods for the spatial and temporal streams respectively. On the one hand, we
observe that regions with action are more important to recognition, and we
propose an optical-flow guided weighted mean-squared-error loss for our
spatial-oriented SR (SoSR) network to emphasize the reconstruction of moving
objects. On the other hand, we observe that existing video SR methods incur
temporal discontinuity between frames, which also worsens the recognition
accuracy, and we propose a siamese network for our temporal-oriented SR (ToSR)
training that emphasizes the temporal continuity between consecutive frames. We
perform experiments using two state-of-the-art action recognition networks and
two well-known datasets--UCF101 and HMDB51. Results demonstrate the
effectiveness of our proposed SoSR and ToSR in improving recognition accuracy.Comment: Accepted to ICCV 2019. Code:
https://github.com/AlanZhang1995/TwoStreamS
Video Surveillance-Based Intelligent Traffic Management in Smart Cities
Visualization of video is considered as important part of visual analytics. Several challenges arise from massive video contents that can be resolved by using data analytics and consequently gaining significance. Though rapid progression in digital technologies resulted in videos data explosion that incites the requirements to create visualization and computer graphics from videos, a state-of-the-art algorithm has been proposed in this chapter for 3D conversion of traffic video contents and displaying on Google Maps. Time stamped visualization based on glyph is employed efficiently in surveillance videos and utilized for event detection. This method of visualization can possibly decrease the complexity of data, having complete view of videos from video collection. The effectiveness of proposed system has shown by obtaining numerous unprocessed videos and algorithm is tested on these videos without concerning field conditions. The proposed visualization technique produces promising results and found effective in conveying meaningful information while alleviating the need of searching exhaustively colossal amount of video data
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