45 research outputs found
Compress-then-analyze vs. analyze-then-compress: Two paradigms for image analysis in visual sensor networks
We compare two paradigms for image analysis in vi- sual sensor networks (VSN). In the compress-then-analyze (CTA) paradigm, images acquired from camera nodes are compressed and sent to a central controller for further analysis. Conversely, in the analyze-then-compress (ATC) approach, camera nodes perform visual feature extraction and transmit a compressed version of these features to a central controller. We focus on state-of-the-art binary features which are particularly suitable for resource-constrained VSNs, and we show that the ”winning” paradigm depends primarily on the network conditions. Indeed, while the ATC approach might be the only possible way to perform analysis at low available bitrates, the CTA approach reaches the best results when the available bandwidth enables the transmission of high-quality images
Coding local and global binary visual features extracted from video sequences
Binary local features represent an effective alternative to real-valued
descriptors, leading to comparable results for many visual analysis tasks,
while being characterized by significantly lower computational complexity and
memory requirements. When dealing with large collections, a more compact
representation based on global features is often preferred, which can be
obtained from local features by means of, e.g., the Bag-of-Visual-Word (BoVW)
model. Several applications, including for example visual sensor networks and
mobile augmented reality, require visual features to be transmitted over a
bandwidth-limited network, thus calling for coding techniques that aim at
reducing the required bit budget, while attaining a target level of efficiency.
In this paper we investigate a coding scheme tailored to both local and global
binary features, which aims at exploiting both spatial and temporal redundancy
by means of intra- and inter-frame coding. In this respect, the proposed coding
scheme can be conveniently adopted to support the Analyze-Then-Compress (ATC)
paradigm. That is, visual features are extracted from the acquired content,
encoded at remote nodes, and finally transmitted to a central controller that
performs visual analysis. This is in contrast with the traditional approach, in
which visual content is acquired at a node, compressed and then sent to a
central unit for further processing, according to the Compress-Then-Analyze
(CTA) paradigm. In this paper we experimentally compare ATC and CTA by means of
rate-efficiency curves in the context of two different visual analysis tasks:
homography estimation and content-based retrieval. Our results show that the
novel ATC paradigm based on the proposed coding primitives can be competitive
with CTA, especially in bandwidth limited scenarios.Comment: submitted to IEEE Transactions on Image Processin
Enabling visual analysis in wireless sensor networks
This demo showcases some of the results obtained by the GreenEyes project, whose main objective is to enable visual analysis on resource-constrained multimedia sensor networks. The demo features a multi-hop visual sensor network operated by BeagleBones Linux computers with IEEE 802.15.4 communication capabilities, and capable of recognizing and tracking objects according to two different visual paradigms. In the traditional compress-then-analyze (CTA) paradigm, JPEG compressed images are transmitted through the network from a camera node to a central controller, where the analysis takes place. In the alternative analyze-then-compress (ATC) paradigm, the camera node extracts and compresses local binary visual features from the acquired images (either locally or in a distributed fashion) and transmits them to the central controller, where they are used to perform object recognition/tracking. We show that, in a bandwidth constrained scenario, the latter paradigm allows to reach better results in terms of application frame rates, still ensuring excellent analysis performance
Hybrid coding of visual content and local image features
Distributed visual analysis applications, such as mobile visual search or
Visual Sensor Networks (VSNs) require the transmission of visual content on a
bandwidth-limited network, from a peripheral node to a processing unit.
Traditionally, a Compress-Then-Analyze approach has been pursued, in which
sensing nodes acquire and encode the pixel-level representation of the visual
content, that is subsequently transmitted to a sink node in order to be
processed. This approach might not represent the most effective solution, since
several analysis applications leverage a compact representation of the content,
thus resulting in an inefficient usage of network resources. Furthermore,
coding artifacts might significantly impact the accuracy of the visual task at
hand. To tackle such limitations, an orthogonal approach named
Analyze-Then-Compress has been proposed. According to such a paradigm, sensing
nodes are responsible for the extraction of visual features, that are encoded
and transmitted to a sink node for further processing. In spite of improved
task efficiency, such paradigm implies the central processing node not being
able to reconstruct a pixel-level representation of the visual content. In this
paper we propose an effective compromise between the two paradigms, namely
Hybrid-Analyze-Then-Compress (HATC) that aims at jointly encoding visual
content and local image features. Furthermore, we show how a target tradeoff
between image quality and task accuracy might be achieved by accurately
allocating the bitrate to either visual content or local features.Comment: submitted to IEEE International Conference on Image Processin