2,727 research outputs found
Deep Detection of People and their Mobility Aids for a Hospital Robot
Robots operating in populated environments encounter many different types of
people, some of whom might have an advanced need for cautious interaction,
because of physical impairments or their advanced age. Robots therefore need to
recognize such advanced demands to provide appropriate assistance, guidance or
other forms of support. In this paper, we propose a depth-based perception
pipeline that estimates the position and velocity of people in the environment
and categorizes them according to the mobility aids they use: pedestrian,
person in wheelchair, person in a wheelchair with a person pushing them, person
with crutches and person using a walker. We present a fast region proposal
method that feeds a Region-based Convolutional Network (Fast R-CNN). With this,
we speed up the object detection process by a factor of seven compared to a
dense sliding window approach. We furthermore propose a probabilistic position,
velocity and class estimator to smooth the CNN's detections and account for
occlusions and misclassifications. In addition, we introduce a new hospital
dataset with over 17,000 annotated RGB-D images. Extensive experiments confirm
that our pipeline successfully keeps track of people and their mobility aids,
even in challenging situations with multiple people from different categories
and frequent occlusions. Videos of our experiments and the dataset are
available at http://www2.informatik.uni-freiburg.de/~kollmitz/MobilityAidsComment: 7 pages, ECMR 2017, dataset and videos:
http://www2.informatik.uni-freiburg.de/~kollmitz/MobilityAids
S-OHEM: Stratified Online Hard Example Mining for Object Detection
One of the major challenges in object detection is to propose detectors with
highly accurate localization of objects. The online sampling of high-loss
region proposals (hard examples) uses the multitask loss with equal weight
settings across all loss types (e.g, classification and localization, rigid and
non-rigid categories) and ignores the influence of different loss distributions
throughout the training process, which we find essential to the training
efficacy. In this paper, we present the Stratified Online Hard Example Mining
(S-OHEM) algorithm for training higher efficiency and accuracy detectors.
S-OHEM exploits OHEM with stratified sampling, a widely-adopted sampling
technique, to choose the training examples according to this influence during
hard example mining, and thus enhance the performance of object detectors. We
show through systematic experiments that S-OHEM yields an average precision
(AP) improvement of 0.5% on rigid categories of PASCAL VOC 2007 for both the
IoU threshold of 0.6 and 0.7. For KITTI 2012, both results of the same metric
are 1.6%. Regarding the mean average precision (mAP), a relative increase of
0.3% and 0.5% (1% and 0.5%) is observed for VOC07 (KITTI12) using the same set
of IoU threshold. Also, S-OHEM is easy to integrate with existing region-based
detectors and is capable of acting with post-recognition level regressors.Comment: 9 pages, 3 figures, accepted by CCCV 201
Multiple Instance Curriculum Learning for Weakly Supervised Object Detection
When supervising an object detector with weakly labeled data, most existing
approaches are prone to trapping in the discriminative object parts, e.g.,
finding the face of a cat instead of the full body, due to lacking the
supervision on the extent of full objects. To address this challenge, we
incorporate object segmentation into the detector training, which guides the
model to correctly localize the full objects. We propose the multiple instance
curriculum learning (MICL) method, which injects curriculum learning (CL) into
the multiple instance learning (MIL) framework. The MICL method starts by
automatically picking the easy training examples, where the extent of the
segmentation masks agree with detection bounding boxes. The training set is
gradually expanded to include harder examples to train strong detectors that
handle complex images. The proposed MICL method with segmentation in the loop
outperforms the state-of-the-art weakly supervised object detectors by a
substantial margin on the PASCAL VOC datasets.Comment: Published in BMVC 201
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