1,472 research outputs found
Hierarchical Adaptive Structural SVM for Domain Adaptation
A key topic in classification is the accuracy loss produced when the data
distribution in the training (source) domain differs from that in the testing
(target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and
object category recognition. In this paper, we present a novel domain
adaptation method that leverages multiple target domains (or sub-domains) in a
hierarchical adaptation tree. The core idea is to exploit the commonalities and
differences of the jointly considered target domains.
Given the relevance of structural SVM (SSVM) classifiers, we apply our idea
to the adaptive SSVM (A-SSVM), which only requires the target domain samples
together with the existing source-domain classifier for performing the desired
adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM).
As proof of concept we use HA-SSVM for pedestrian detection and object
category recognition. In the former we apply HA-SSVM to the deformable
part-based model (DPM) while in the latter HA-SSVM is applied to multi-category
classifiers. In both cases, we show how HA-SSVM is effective in increasing the
detection/recognition accuracy with respect to adaptation strategies that
ignore the structure of the target data. Since, the sub-domains of the target
data are not always known a priori, we shown how HA-SSVM can incorporate
sub-domain structure discovery for object category recognition
Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash
Human lives are important. The decision to allow self-driving vehicles
operate on our roads carries great weight. This has been a hot topic of debate
between policy-makers, technologists and public safety institutions. The recent
Uber Inc. self-driving car crash, resulting in the death of a pedestrian, has
strengthened the argument that autonomous vehicle technology is still not ready
for deployment on public roads. In this work, we analyze the Uber car crash and
shed light on the question, "Could the Uber Car Crash have been avoided?". We
apply state-of-the-art Computer Vision models to this highly practical
scenario. More generally, our experimental results are an evaluation of various
image enhancement and object recognition techniques for enabling pedestrian
safety in low-lighting conditions using the Uber crash as a case study.Comment: 10 pages, 8 figures, 3 table
Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning
Avoiding collisions with vulnerable road users (VRUs) using sensor-based
early recognition of critical situations is one of the manifold opportunities
provided by the current development in the field of intelligent vehicles. As
especially pedestrians and cyclists are very agile and have a variety of
movement options, modeling their behavior in traffic scenes is a challenging
task. In this article we propose movement models based on machine learning
methods, in particular artificial neural networks, in order to classify the
current motion state and to predict the future trajectory of VRUs. Both model
types are also combined to enable the application of specifically trained
motion predictors based on a continuously updated pseudo probabilistic state
classification. Furthermore, the architecture is used to evaluate
motion-specific physical models for starting and stopping and video-based
pedestrian motion classification. A comprehensive dataset consisting of 1068
pedestrian and 494 cyclist scenes acquired at an urban intersection is used for
optimization, training, and evaluation of the different models. The results
show substantial higher classification rates and the ability to earlier
recognize motion state changes with the machine learning approaches compared to
interacting multiple model (IMM) Kalman Filtering. The trajectory prediction
quality is also improved for all kinds of test scenes, especially when starting
and stopping motions are included. Here, 37\% and 41\% lower position errors
were achieved on average, respectively
Incremental Learning Through Deep Adaptation
Given an existing trained neural network, it is often desirable to learn new
capabilities without hindering performance of those already learned. Existing
approaches either learn sub-optimal solutions, require joint training, or incur
a substantial increment in the number of parameters for each added domain,
typically as many as the original network. We propose a method called
\emph{Deep Adaptation Networks} (DAN) that constrains newly learned filters to
be linear combinations of existing ones. DANs precisely preserve performance on
the original domain, require a fraction (typically 13\%, dependent on network
architecture) of the number of parameters compared to standard fine-tuning
procedures and converge in less cycles of training to a comparable or better
level of performance. When coupled with standard network quantization
techniques, we further reduce the parameter cost to around 3\% of the original
with negligible or no loss in accuracy. The learned architecture can be
controlled to switch between various learned representations, enabling a single
network to solve a task from multiple different domains. We conduct extensive
experiments showing the effectiveness of our method on a range of image
classification tasks and explore different aspects of its behavior.Comment: Extended versio
Deep Learning for Generic Object Detection: A Survey
Object detection, one of the most fundamental and challenging problems in
computer vision, seeks to locate object instances from a large number of
predefined categories in natural images. Deep learning techniques have emerged
as a powerful strategy for learning feature representations directly from data
and have led to remarkable breakthroughs in the field of generic object
detection. Given this period of rapid evolution, the goal of this paper is to
provide a comprehensive survey of the recent achievements in this field brought
about by deep learning techniques. More than 300 research contributions are
included in this survey, covering many aspects of generic object detection:
detection frameworks, object feature representation, object proposal
generation, context modeling, training strategies, and evaluation metrics. We
finish the survey by identifying promising directions for future research.Comment: IJCV Mino
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection
In this paper, we propose multi-stage and deformable deep convolutional
neural networks for object detection. This new deep learning object detection
diagram has innovations in multiple aspects. In the proposed new deep
architecture, a new deformation constrained pooling (def-pooling) layer models
the deformation of object parts with geometric constraint and penalty. With the
proposed multi-stage training strategy, multiple classifiers are jointly
optimized to process samples at different difficulty levels. A new pre-training
strategy is proposed to learn feature representations more suitable for the
object detection task and with good generalization capability. By changing the
net structures, training strategies, adding and removing some key components in
the detection pipeline, a set of models with large diversity are obtained,
which significantly improves the effectiveness of modeling averaging. The
proposed approach ranked \#2 in ILSVRC 2014. It improves the mean averaged
precision obtained by RCNN, which is the state-of-the-art of object detection,
from to . Detailed component-wise analysis is also provided
through extensive experimental evaluation
Understanding Urban Human Mobility through Crowdsensed Data
Understanding how people move in the urban area is important for solving
urbanization issues, such as traffic management, urban planning, epidemic
control, and communication network improvement. Leveraging recent availability
of large amounts of diverse crowdsensed data, many studies have made
contributions to this field in various aspects. They need proper review and
summary. In this paper, therefore, we first review these recent studies with a
proper taxonomy with corresponding examples. Then, based on the experience
learnt from the studies, we provide a comprehensive tutorial for future
research, which introduces and discusses popular crowdsensed data types,
different human mobility subjects, and common data preprocessing and analysis
methods. Special emphasis is made on the matching between data types and
mobility subjects. Finally, we present two research projects as case studies to
demonstrate the entire process of understanding urban human mobility through
crowdsensed data in city-wide scale and building-wide scale respectively.
Beyond demonstration purpose, the two case studies also make contributions to
their category of certain crowdsensed data type and mobility subject.Comment: This manuscript is published in IEEE Communications Magazine 56.11
(2018): 52-59. Please refer to the published version at
https://ieeexplore.ieee.org/abstract/document/853902
A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles
Vehicle to Vehicle (V2V) communication has a great potential to improve
reaction accuracy of different driver assistance systems in critical driving
situations. Cooperative Adaptive Cruise Control (CACC), which is an automated
application, provides drivers with extra benefits such as traffic throughput
maximization and collision avoidance. CACC systems must be designed in a way
that are sufficiently robust against all special maneuvers such as cutting-into
the CACC platoons by interfering vehicles or hard braking by leading cars. To
address this problem, a Neural- Network (NN)-based cut-in detection and
trajectory prediction scheme is proposed in the first part of this paper. Next,
a probabilistic framework is developed in which the cut-in probability is
calculated based on the output of the mentioned cut-in prediction block.
Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed
which incorporates this cut-in probability to enhance its reaction against the
detected dangerous cut-in maneuver. The overall system is implemented and its
performance is evaluated using realistic driving scenarios from Safety Pilot
Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I
VADRA: Visual Adversarial Domain Randomization and Augmentation
We address the issue of learning from synthetic domain randomized data
effectively. While previous works have showcased domain randomization as an
effective learning approach, it lacks in challenging the learner and wastes
valuable compute on generating easy examples. This can be attributed to uniform
randomization over the rendering parameter distribution. In this work, firstly
we provide a theoretical perspective on characteristics of domain randomization
and analyze its limitations. As a solution to these limitations, we propose a
novel algorithm which closes the loop between the synthetic generative model
and the learner in an adversarial fashion. Our framework easily extends to the
scenario when there is unlabelled target data available, thus incorporating
domain adaptation. We evaluate our method on diverse vision tasks using
state-of-the-art simulators for public datasets like CLEVR, Syn2Real, and
VIRAT, where we demonstrate that a learner trained using adversarial data
generation performs better than using a random data generation strategy
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In
2015 and 2016, we thoroughly study 1,600+ papers in several
conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV
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