14,975 research outputs found
Reduced Memory Region Based Deep Convolutional Neural Network Detection
Accurate pedestrian detection has a primary role in automotive safety: for
example, by issuing warnings to the driver or acting actively on car's brakes,
it helps decreasing the probability of injuries and human fatalities. In order
to achieve very high accuracy, recent pedestrian detectors have been based on
Convolutional Neural Networks (CNN). Unfortunately, such approaches require
vast amounts of computational power and memory, preventing efficient
implementations on embedded systems. This work proposes a CNN-based detector,
adapting a general-purpose convolutional network to the task at hand. By
thoroughly analyzing and optimizing each step of the detection pipeline, we
develop an architecture that outperforms methods based on traditional image
features and achieves an accuracy close to the state-of-the-art while having
low computational complexity. Furthermore, the model is compressed in order to
fit the tight constrains of low power devices with a limited amount of embedded
memory available. This paper makes two main contributions: (1) it proves that a
region based deep neural network can be finely tuned to achieve adequate
accuracy for pedestrian detection (2) it achieves a very low memory usage
without reducing detection accuracy on the Caltech Pedestrian dataset.Comment: IEEE 2016 ICCE-Berli
Blending Learning and Inference in Structured Prediction
In this paper we derive an efficient algorithm to learn the parameters of
structured predictors in general graphical models. This algorithm blends the
learning and inference tasks, which results in a significant speedup over
traditional approaches, such as conditional random fields and structured
support vector machines. For this purpose we utilize the structures of the
predictors to describe a low dimensional structured prediction task which
encourages local consistencies within the different structures while learning
the parameters of the model. Convexity of the learning task provides the means
to enforce the consistencies between the different parts. The
inference-learning blending algorithm that we propose is guaranteed to converge
to the optimum of the low dimensional primal and dual programs. Unlike many of
the existing approaches, the inference-learning blending allows us to learn
efficiently high-order graphical models, over regions of any size, and very
large number of parameters. We demonstrate the effectiveness of our approach,
while presenting state-of-the-art results in stereo estimation, semantic
segmentation, shape reconstruction, and indoor scene understanding
Multi-Person Pose Estimation with Local Joint-to-Person Associations
Despite of the recent success of neural networks for human pose estimation,
current approaches are limited to pose estimation of a single person and cannot
handle humans in groups or crowds. In this work, we propose a method that
estimates the poses of multiple persons in an image in which a person can be
occluded by another person or might be truncated. To this end, we consider
multi-person pose estimation as a joint-to-person association problem. We
construct a fully connected graph from a set of detected joint candidates in an
image and resolve the joint-to-person association and outlier detection using
integer linear programming. Since solving joint-to-person association jointly
for all persons in an image is an NP-hard problem and even approximations are
expensive, we solve the problem locally for each person. On the challenging
MPII Human Pose Dataset for multiple persons, our approach achieves the
accuracy of a state-of-the-art method, but it is 6,000 to 19,000 times faster.Comment: Accepted to European Conference on Computer Vision (ECCV) Workshops,
Crowd Understanding, 201
Multiscale Fields of Patterns
We describe a framework for defining high-order image models that can be used
in a variety of applications. The approach involves modeling local patterns in
a multiscale representation of an image. Local properties of a coarsened image
reflect non-local properties of the original image. In the case of binary
images local properties are defined by the binary patterns observed over small
neighborhoods around each pixel. With the multiscale representation we capture
the frequency of patterns observed at different scales of resolution. This
framework leads to expressive priors that depend on a relatively small number
of parameters. For inference and learning we use an MCMC method for block
sampling with very large blocks. We evaluate the approach with two example
applications. One involves contour detection. The other involves binary
segmentation.Comment: In NIPS 201
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