770 research outputs found
Learning Depth from Monocular Videos using Direct Methods
The ability to predict depth from a single image - using recent advances in
CNNs - is of increasing interest to the vision community. Unsupervised
strategies to learning are particularly appealing as they can utilize much
larger and varied monocular video datasets during learning without the need for
ground truth depth or stereo. In previous works, separate pose and depth CNN
predictors had to be determined such that their joint outputs minimized the
photometric error. Inspired by recent advances in direct visual odometry (DVO),
we argue that the depth CNN predictor can be learned without a pose CNN
predictor. Further, we demonstrate empirically that incorporation of a
differentiable implementation of DVO, along with a novel depth normalization
strategy - substantially improves performance over state of the art that use
monocular videos for training
Deep-LK for Efficient Adaptive Object Tracking
In this paper we present a new approach for efficient regression based object
tracking which we refer to as Deep- LK. Our approach is closely related to the
Generic Object Tracking Using Regression Networks (GOTURN) framework of Held et
al. We make the following contributions. First, we demonstrate that there is a
theoretical relationship between siamese regression networks like GOTURN and
the classical Inverse-Compositional Lucas & Kanade (IC-LK) algorithm. Further,
we demonstrate that unlike GOTURN IC-LK adapts its regressor to the appearance
of the currently tracked frame. We argue that this missing property in GOTURN
can be attributed to its poor performance on unseen objects and/or viewpoints.
Second, we propose a novel framework for object tracking - which we refer to as
Deep-LK - that is inspired by the IC-LK framework. Finally, we show impressive
results demonstrating that Deep-LK substantially outperforms GOTURN.
Additionally, we demonstrate comparable tracking performance to current state
of the art deep-trackers whilst being an order of magnitude (i.e. 100 FPS)
computationally efficient
Place recognition: An Overview of Vision Perspective
Place recognition is one of the most fundamental topics in computer vision
and robotics communities, where the task is to accurately and efficiently
recognize the location of a given query image. Despite years of wisdom
accumulated in this field, place recognition still remains an open problem due
to the various ways in which the appearance of real-world places may differ.
This paper presents an overview of the place recognition literature. Since
condition invariant and viewpoint invariant features are essential factors to
long-term robust visual place recognition system, We start with traditional
image description methodology developed in the past, which exploit techniques
from image retrieval field. Recently, the rapid advances of related fields such
as object detection and image classification have inspired a new technique to
improve visual place recognition system, i.e., convolutional neural networks
(CNNs). Thus we then introduce recent progress of visual place recognition
system based on CNNs to automatically learn better image representations for
places. Eventually, we close with discussions and future work of place
recognition.Comment: Applied Sciences (2018
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
The region-based Convolutional Neural Network (CNN) detectors such as Faster
R-CNN or R-FCN have already shown promising results for object detection by
combining the region proposal subnetwork and the classification subnetwork
together. Although R-FCN has achieved higher detection speed while keeping the
detection performance, the global structure information is ignored by the
position-sensitive score maps. To fully explore the local and global
properties, in this paper, we propose a novel fully convolutional network,
named as CoupleNet, to couple the global structure with local parts for object
detection. Specifically, the object proposals obtained by the Region Proposal
Network (RPN) are fed into the the coupling module which consists of two
branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to
capture the local part information of the object, while the other employs the
RoI pooling to encode the global and context information. Next, we design
different coupling strategies and normalization ways to make full use of the
complementary advantages between the global and local branches. Extensive
experiments demonstrate the effectiveness of our approach. We achieve
state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7%
on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly
available.Comment: Accepted by ICCV 201
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