297 research outputs found
What Can Help Pedestrian Detection?
Aggregating extra features has been considered as an effective approach to
boost traditional pedestrian detection methods. However, there is still a lack
of studies on whether and how CNN-based pedestrian detectors can benefit from
these extra features. The first contribution of this paper is exploring this
issue by aggregating extra features into CNN-based pedestrian detection
framework. Through extensive experiments, we evaluate the effects of different
kinds of extra features quantitatively. Moreover, we propose a novel network
architecture, namely HyperLearner, to jointly learn pedestrian detection as
well as the given extra feature. By multi-task training, HyperLearner is able
to utilize the information of given features and improve detection performance
without extra inputs in inference. The experimental results on multiple
pedestrian benchmarks validate the effectiveness of the proposed HyperLearner.Comment: Accepted to IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 201
Repulsion Loss: Detecting Pedestrians in a Crowd
Detecting individual pedestrians in a crowd remains a challenging problem
since the pedestrians often gather together and occlude each other in
real-world scenarios. In this paper, we first explore how a state-of-the-art
pedestrian detector is harmed by crowd occlusion via experimentation, providing
insights into the crowd occlusion problem. Then, we propose a novel bounding
box regression loss specifically designed for crowd scenes, termed repulsion
loss. This loss is driven by two motivations: the attraction by target, and the
repulsion by other surrounding objects. The repulsion term prevents the
proposal from shifting to surrounding objects thus leading to more crowd-robust
localization. Our detector trained by repulsion loss outperforms all the
state-of-the-art methods with a significant improvement in occlusion cases.Comment: Accepted to IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
FoveaBox: Beyond Anchor-based Object Detector
We present FoveaBox, an accurate, flexible, and completely anchor-free
framework for object detection. While almost all state-of-the-art object
detectors utilize predefined anchors to enumerate possible locations, scales
and aspect ratios for the search of the objects, their performance and
generalization ability are also limited to the design of anchors. Instead,
FoveaBox directly learns the object existing possibility and the bounding box
coordinates without anchor reference. This is achieved by: (a) predicting
category-sensitive semantic maps for the object existing possibility, and (b)
producing category-agnostic bounding box for each position that potentially
contains an object. The scales of target boxes are naturally associated with
feature pyramid representations. In FoveaBox, an instance is assigned to
adjacent feature levels to make the model more accurate.We demonstrate its
effectiveness on standard benchmarks and report extensive experimental
analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single
model performance on the standard COCO and Pascal VOC object detection
benchmark. More importantly, FoveaBox avoids all computation and
hyper-parameters related to anchor boxes, which are often sensitive to the
final detection performance. We believe the simple and effective approach will
serve as a solid baseline and help ease future research for object detection.
The code has been made publicly available at
https://github.com/taokong/FoveaBox .Comment: IEEE Transactions on Image Processing, code at:
https://github.com/taokong/FoveaBo
Distributed Control Enforcing Group Sparsity in Smart Grids
In modern smart grids, charging of local energy storage devices is
coordinated on a residential level to compensate the volatile aggregated power
demand on the time interval of interest. However, this results in a perpetual
usage of all batteries which reduces their lifetime. We enforce group sparsity
by using an -regularization on the control to counteract this
phenomenon. This leads to a non-smooth convex optimization problem, for which
we propose a tailored Alternating Direction Method of Multipliers algorithm. We
elaborate further how to embed it in a Model Predictive Control framework. We
show that the proposed scheme yields sparse control while achieving reasonable
overall peak shaving by numerical simulations
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