31,970 research outputs found
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
Learning Active Basis Models by EM-Type Algorithms
EM algorithm is a convenient tool for maximum likelihood model fitting when
the data are incomplete or when there are latent variables or hidden states. In
this review article we explain that EM algorithm is a natural computational
scheme for learning image templates of object categories where the learning is
not fully supervised. We represent an image template by an active basis model,
which is a linear composition of a selected set of localized, elongated and
oriented wavelet elements that are allowed to slightly perturb their locations
and orientations to account for the deformations of object shapes. The model
can be easily learned when the objects in the training images are of the same
pose, and appear at the same location and scale. This is often called
supervised learning. In the situation where the objects may appear at different
unknown locations, orientations and scales in the training images, we have to
incorporate the unknown locations, orientations and scales as latent variables
into the image generation process, and learn the template by EM-type
algorithms. The E-step imputes the unknown locations, orientations and scales
based on the currently learned template. This step can be considered
self-supervision, which involves using the current template to recognize the
objects in the training images. The M-step then relearns the template based on
the imputed locations, orientations and scales, and this is essentially the
same as supervised learning. So the EM learning process iterates between
recognition and supervised learning. We illustrate this scheme by several
experiments.Comment: Published in at http://dx.doi.org/10.1214/09-STS281 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Comparator Networks
The objective of this work is set-based verification, e.g. to decide if two
sets of images of a face are of the same person or not. The traditional
approach to this problem is to learn to generate a feature vector per image,
aggregate them into one vector to represent the set, and then compute the
cosine similarity between sets. Instead, we design a neural network
architecture that can directly learn set-wise verification. Our contributions
are: (i) We propose a Deep Comparator Network (DCN) that can ingest a pair of
sets (each may contain a variable number of images) as inputs, and compute a
similarity between the pair--this involves attending to multiple discriminative
local regions (landmarks), and comparing local descriptors between pairs of
faces; (ii) To encourage high-quality representations for each set, internal
competition is introduced for recalibration based on the landmark score; (iii)
Inspired by image retrieval, a novel hard sample mining regime is proposed to
control the sampling process, such that the DCN is complementary to the
standard image classification models. Evaluations on the IARPA Janus face
recognition benchmarks show that the comparator networks outperform the
previous state-of-the-art results by a large margin.Comment: To appear in ECCV 201
Food Recognition using Fusion of Classifiers based on CNNs
With the arrival of convolutional neural networks, the complex problem of
food recognition has experienced an important improvement in recent years. The
best results have been obtained using methods based on very deep convolutional
neural networks, which show that the deeper the model,the better the
classification accuracy will be obtain. However, very deep neural networks may
suffer from the overfitting problem. In this paper, we propose a combination of
multiple classifiers based on different convolutional models that complement
each other and thus, achieve an improvement in performance. The evaluation of
our approach is done on two public datasets: Food-101 as a dataset with a wide
variety of fine-grained dishes, and Food-11 as a dataset of high-level food
categories, where our approach outperforms the independent CNN models
Featureless visual processing for SLAM in changing outdoor environments
Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features
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