3,815 research outputs found
A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images
Predictive coding is attractive for compression onboard of spacecrafts thanks
to its low computational complexity, modest memory requirements and the ability
to accurately control quality on a pixel-by-pixel basis. Traditionally,
predictive compression focused on the lossless and near-lossless modes of
operation where the maximum error can be bounded but the rate of the compressed
image is variable. Rate control is considered a challenging problem for
predictive encoders due to the dependencies between quantization and prediction
in the feedback loop, and the lack of a signal representation that packs the
signal's energy into few coefficients. In this paper, we show that it is
possible to design a rate control scheme intended for onboard implementation.
In particular, we propose a general framework to select quantizers in each
spatial and spectral region of an image so as to achieve the desired target
rate while minimizing distortion. The rate control algorithm allows to achieve
lossy, near-lossless compression, and any in-between type of compression, e.g.,
lossy compression with a near-lossless constraint. While this framework is
independent of the specific predictor used, in order to show its performance,
in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless
compression standard, obtaining an extension that allows to perform lossless,
near-lossless and lossy compression in a single package. We show that the rate
controller has excellent performance in terms of accuracy in the output rate,
rate-distortion characteristics and is extremely competitive with respect to
state-of-the-art transform coding
Real-time Person Re-identification at the Edge: A Mixed Precision Approach
A critical part of multi-person multi-camera tracking is person
re-identification (re-ID) algorithm, which recognizes and retains identities of
all detected unknown people throughout the video stream. Many re-ID algorithms
today exemplify state of the art results, but not much work has been done to
explore the deployment of such algorithms for computation and power constrained
real-time scenarios. In this paper, we study the effect of using a light-weight
model, MobileNet-v2 for re-ID and investigate the impact of single (FP32)
precision versus half (FP16) precision for training on the server and inference
on the edge nodes. We further compare the results with the baseline model which
uses ResNet-50 on state of the art benchmarks including CUHK03, Market-1501,
and Duke-MTMC. The MobileNet-V2 mixed precision training method can improve
both inference throughput on the edge node, and training time on server
reaching to 27.77fps and , respectively and decreases
power consumption on the edge node by , while it deteriorates
accuracy only 5.6\% in respect to ResNet-50 single precision on the average for
three different datasets. The code and pre-trained networks are publicly
available at https://github.com/TeCSAR-UNCC/person-reid.Comment: This is a pre-print of an article published in International
Conference on Image Analysis and Recognition (ICIAR 2019), Lecture Notes in
Computer Science. The final authenticated version is available online at
https://doi.org/10.1007/978-3-030-27272-2_
Extrinisic Calibration of a Camera-Arm System Through Rotation Identification
Determining extrinsic calibration parameters is a necessity in any robotic
system composed of actuators and cameras. Once a system is outside the lab
environment, parameters must be determined without relying on outside artifacts
such as calibration targets. We propose a method that relies on structured
motion of an observed arm to recover extrinsic calibration parameters. Our
method combines known arm kinematics with observations of conics in the image
plane to calculate maximum-likelihood estimates for calibration extrinsics.
This method is validated in simulation and tested against a real-world model,
yielding results consistent with ruler-based estimates. Our method shows
promise for estimating the pose of a camera relative to an articulated arm's
end effector without requiring tedious measurements or external artifacts.
Index Terms: robotics, hand-eye problem, self-calibration, structure from
motio
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