5,687 research outputs found
Fast object detection in compressed JPEG Images
Object detection in still images has drawn a lot of attention over past few
years, and with the advent of Deep Learning impressive performances have been
achieved with numerous industrial applications. Most of these deep learning
models rely on RGB images to localize and identify objects in the image.
However in some application scenarii, images are compressed either for storage
savings or fast transmission. Therefore a time consuming image decompression
step is compulsory in order to apply the aforementioned deep models. To
alleviate this drawback, we propose a fast deep architecture for object
detection in JPEG images, one of the most widespread compression format. We
train a neural network to detect objects based on the blockwise DCT (discrete
cosine transform) coefficients {issued from} the JPEG compression algorithm. We
modify the well-known Single Shot multibox Detector (SSD) by replacing its
first layers with one convolutional layer dedicated to process the DCT inputs.
Experimental evaluations on PASCAL VOC and industrial dataset comprising images
of road traffic surveillance show that the model is about faster than
regular SSD with promising detection performances. To the best of our
knowledge, this paper is the first to address detection in compressed JPEG
images
From temporal network data to the dynamics of social relationships
Networks are well-established representations of social systems, and temporal
networks are widely used to study their dynamics. Temporal network data often
consist in a succession of static networks over consecutive time windows whose
length, however, is arbitrary, not necessarily corresponding to any intrinsic
timescale of the system. Moreover, the resulting view of social network
evolution is unsatisfactory: short time windows contain little information,
whereas aggregating over large time windows blurs the dynamics. Going from a
temporal network to a meaningful evolving representation of a social network
therefore remains a challenge. Here we introduce a framework to that purpose:
transforming temporal network data into an evolving weighted network where the
weights of the links between individuals are updated at every interaction. Most
importantly, this transformation takes into account the interdependence of
social relationships due to the finite attention capacities of individuals:
each interaction between two individuals not only reinforces their mutual
relationship but also weakens their relationships with others. We study a
concrete example of such a transformation and apply it to several data sets of
social interactions. Using temporal contact data collected in schools, we show
how our framework highlights specificities in their structure and temporal
organization. We then introduce a synthetic perturbation into a data set of
interactions in a group of baboons to show that it is possible to detect a
perturbation in a social group on a wide range of timescales and parameters.
Our framework brings new perspectives to the analysis of temporal social
networks
Low-latency compression of mocap data using learned spatial decorrelation transform
Due to the growing needs of human motion capture (mocap) in movie, video
games, sports, etc., it is highly desired to compress mocap data for efficient
storage and transmission. This paper presents two efficient frameworks for
compressing human mocap data with low latency. The first framework processes
the data in a frame-by-frame manner so that it is ideal for mocap data
streaming and time critical applications. The second one is clip-based and
provides a flexible tradeoff between latency and compression performance. Since
mocap data exhibits some unique spatial characteristics, we propose a very
effective transform, namely learned orthogonal transform (LOT), for reducing
the spatial redundancy. The LOT problem is formulated as minimizing square
error regularized by orthogonality and sparsity and solved via alternating
iteration. We also adopt a predictive coding and temporal DCT for temporal
decorrelation in the frame- and clip-based frameworks, respectively.
Experimental results show that the proposed frameworks can produce higher
compression performance at lower computational cost and latency than the
state-of-the-art methods.Comment: 15 pages, 9 figure
- …