4,688 research outputs found
Gaussian Processes with Context-Supported Priors for Active Object Localization
We devise an algorithm using a Bayesian optimization framework in conjunction
with contextual visual data for the efficient localization of objects in still
images. Recent research has demonstrated substantial progress in object
localization and related tasks for computer vision. However, many current
state-of-the-art object localization procedures still suffer from inaccuracy
and inefficiency, in addition to failing to provide a principled and
interpretable system amenable to high-level vision tasks. We address these
issues with the current research.
Our method encompasses an active search procedure that uses contextual data
to generate initial bounding-box proposals for a target object. We train a
convolutional neural network to approximate an offset distance from the target
object. Next, we use a Gaussian Process to model this offset response signal
over the search space of the target. We then employ a Bayesian active search
for accurate localization of the target.
In experiments, we compare our approach to a state-of-theart bounding-box
regression method for a challenging pedestrian localization task. Our method
exhibits a substantial improvement over this baseline regression method.Comment: 10 pages, 4 figure
Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection
Pedestrian detection is an important component for safety of autonomous
vehicles, as well as for traffic and street surveillance. There are extensive
benchmarks on this topic and it has been shown to be a challenging problem when
applied on real use-case scenarios. In purely image-based pedestrian detection
approaches, the state-of-the-art results have been achieved with convolutional
neural networks (CNN) and surprisingly few detection frameworks have been built
upon multi-cue approaches. In this work, we develop a new pedestrian detector
for autonomous vehicles that exploits LiDAR data, in addition to visual
information. In the proposed approach, LiDAR data is utilized to generate
region proposals by processing the three dimensional point cloud that it
provides. These candidate regions are then further processed by a
state-of-the-art CNN classifier that we have fine-tuned for pedestrian
detection. We have extensively evaluated the proposed detection process on the
KITTI dataset. The experimental results show that the proposed LiDAR space
clustering approach provides a very efficient way of generating region
proposals leading to higher recall rates and fewer misses for pedestrian
detection. This indicates that LiDAR data can provide auxiliary information for
CNN-based approaches
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