1,017 research outputs found
Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery
Automatic multi-class object detection in remote sensing images in
unconstrained scenarios is of high interest for several applications including
traffic monitoring and disaster management. The huge variation in object scale,
orientation, category, and complex backgrounds, as well as the different camera
sensors pose great challenges for current algorithms. In this work, we propose
a new method consisting of a novel joint image cascade and feature pyramid
network with multi-size convolution kernels to extract multi-scale strong and
weak semantic features. These features are fed into rotation-based region
proposal and region of interest networks to produce object detections. Finally,
rotational non-maximum suppression is applied to remove redundant detections.
During training, we minimize joint horizontal and oriented bounding box loss
functions, as well as a novel loss that enforces oriented boxes to be
rectangular. Our method achieves 68.16% mAP on horizontal and 72.45% mAP on
oriented bounding box detection tasks on the challenging DOTA dataset,
outperforming all published methods by a large margin (+6% and +12% absolute
improvement, respectively). Furthermore, it generalizes to two other datasets,
NWPU VHR-10 and UCAS-AOD, and achieves competitive results with the baselines
even when trained on DOTA. Our method can be deployed in multi-class object
detection applications, regardless of the image and object scales and
orientations, making it a great choice for unconstrained aerial and satellite
imagery.Comment: ACCV 201
Multi-view Face Detection Using Deep Convolutional Neural Networks
In this paper we consider the problem of multi-view face detection. While
there has been significant research on this problem, current state-of-the-art
approaches for this task require annotation of facial landmarks, e.g. TSM [25],
or annotation of face poses [28, 22]. They also require training dozens of
models to fully capture faces in all orientations, e.g. 22 models in HeadHunter
method [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method
that does not require pose/landmark annotation and is able to detect faces in a
wide range of orientations using a single model based on deep convolutional
neural networks. The proposed method has minimal complexity; unlike other
recent deep learning object detection methods [9], it does not require
additional components such as segmentation, bounding-box regression, or SVM
classifiers. Furthermore, we analyzed scores of the proposed face detector for
faces in different orientations and found that 1) the proposed method is able
to detect faces from different angles and can handle occlusion to some extent,
2) there seems to be a correlation between dis- tribution of positive examples
in the training set and scores of the proposed face detector. The latter
suggests that the proposed methods performance can be further improved by using
better sampling strategies and more sophisticated data augmentation techniques.
Evaluations on popular face detection benchmark datasets show that our
single-model face detector algorithm has similar or better performance compared
to the previous methods, which are more complex and require annotations of
either different poses or facial landmarks.Comment: in International Conference on Multimedia Retrieval 2015 (ICMR
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
Robust face detection in the wild is one of the ultimate components to
support various facial related problems, i.e. unconstrained face recognition,
facial periocular recognition, facial landmarking and pose estimation, facial
expression recognition, 3D facial model construction, etc. Although the face
detection problem has been intensely studied for decades with various
commercial applications, it still meets problems in some real-world scenarios
due to numerous challenges, e.g. heavy facial occlusions, extremely low
resolutions, strong illumination, exceptionally pose variations, image or video
compression artifacts, etc. In this paper, we present a face detection approach
named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN)
to robustly solve the problems mentioned above. Similar to the region-based
CNNs, our proposed network consists of the region proposal component and the
region-of-interest (RoI) detection component. However, far apart of that
network, there are two main contributions in our proposed network that play a
significant role to achieve the state-of-the-art performance in face detection.
Firstly, the multi-scale information is grouped both in region proposal and RoI
detection to deal with tiny face regions. Secondly, our proposed network allows
explicit body contextual reasoning in the network inspired from the intuition
of human vision system. The proposed approach is benchmarked on two recent
challenging face detection databases, i.e. the WIDER FACE Dataset which
contains high degree of variability, as well as the Face Detection Dataset and
Benchmark (FDDB). The experimental results show that our proposed approach
trained on WIDER FACE Dataset outperforms strong baselines on WIDER FACE
Dataset by a large margin, and consistently achieves competitive results on
FDDB against the recent state-of-the-art face detection methods
Spatio-Temporal Action Detection with Cascade Proposal and Location Anticipation
In this work, we address the problem of spatio-temporal action detection in
temporally untrimmed videos. It is an important and challenging task as finding
accurate human actions in both temporal and spatial space is important for
analyzing large-scale video data. To tackle this problem, we propose a cascade
proposal and location anticipation (CPLA) model for frame-level action
detection. There are several salient points of our model: (1) a cascade region
proposal network (casRPN) is adopted for action proposal generation and shows
better localization accuracy compared with single region proposal network
(RPN); (2) action spatio-temporal consistencies are exploited via a location
anticipation network (LAN) and thus frame-level action detection is not
conducted independently. Frame-level detections are then linked by solving an
linking score maximization problem, and temporally trimmed into spatio-temporal
action tubes. We demonstrate the effectiveness of our model on the challenging
UCF101 and LIRIS-HARL datasets, both achieving state-of-the-art performance.Comment: Accepted at BMVC 2017 (oral
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