1,841 research outputs found
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
High-level feature detection from video in TRECVid: a 5-year retrospective of achievements
Successful and effective content-based access to digital
video requires fast, accurate and scalable methods to determine the video content automatically. A variety of contemporary approaches to this rely on text taken from speech within the video, or on matching one video frame against others using low-level characteristics like
colour, texture, or shapes, or on determining and matching objects appearing within the video. Possibly the most important technique, however, is one which determines the presence or absence of a high-level or semantic feature, within a video clip or shot. By utilizing dozens, hundreds or even thousands of such semantic features we can support many kinds of content-based video navigation. Critically however, this depends on being able to determine whether each feature is or is not present in a video clip.
The last 5 years have seen much progress in the development of techniques to determine the presence of semantic features within video. This progress can be tracked in the annual TRECVid benchmarking activity where dozens of research groups measure the effectiveness of their techniques on common data and using an open, metrics-based approach. In this chapter we summarise the work
done on the TRECVid high-level feature task, showing the
progress made year-on-year. This provides a fairly comprehensive statement on where the state-of-the-art is regarding this important task, not just for one research group or for one approach, but across the spectrum. We then use this past and on-going work as a basis for highlighting the trends that are emerging in this area, and the questions which remain to be addressed before we can
achieve large-scale, fast and reliable high-level feature detection on video
Fine-Grained Head Pose Estimation Without Keypoints
Estimating the head pose of a person is a crucial problem that has a large
amount of applications such as aiding in gaze estimation, modeling attention,
fitting 3D models to video and performing face alignment. Traditionally head
pose is computed by estimating some keypoints from the target face and solving
the 2D to 3D correspondence problem with a mean human head model. We argue that
this is a fragile method because it relies entirely on landmark detection
performance, the extraneous head model and an ad-hoc fitting step. We present
an elegant and robust way to determine pose by training a multi-loss
convolutional neural network on 300W-LP, a large synthetically expanded
dataset, to predict intrinsic Euler angles (yaw, pitch and roll) directly from
image intensities through joint binned pose classification and regression. We
present empirical tests on common in-the-wild pose benchmark datasets which
show state-of-the-art results. Additionally we test our method on a dataset
usually used for pose estimation using depth and start to close the gap with
state-of-the-art depth pose methods. We open-source our training and testing
code as well as release our pre-trained models.Comment: Accepted to Computer Vision and Pattern Recognition Workshops
(CVPRW), 2018 IEEE Conference on. IEEE, 201
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
MPSN: Motion-aware Pseudo Siamese Network for Indoor Video Head Detection in Buildings
Head detection in the indoor video is an essential component of building
occupancy detection. While deep models have achieved remarkable progress in
general object detection, they are not satisfying enough in complex indoor
scenes. The indoor surveillance video often includes cluttered background
objects, among which heads have small scales and diverse poses. In this paper,
we propose Motion-aware Pseudo Siamese Network (MPSN), an end-to-end approach
that leverages head motion information to guide the deep model to extract
effective head features in indoor scenarios. By taking the pixel-wise
difference of adjacent frames as the auxiliary input, MPSN effectively enhances
human head motion information and removes the irrelevant objects in the
background. Compared with prior methods, it achieves superior performance on
the two indoor video datasets. Our experiments show that MPSN successfully
suppresses static background objects and highlights the moving instances,
especially human heads in indoor videos. We also compare different methods to
capture head motion, which demonstrates the simplicity and flexibility of MPSN.
Finally, to validate the robustness of MPSN, we conduct adversarial experiments
with a mathematical solution of small perturbations for robust model selection.
Code is available at https://github.com/pl-share/MPSN
Application-aware optimization of Artificial Intelligence for deployment on resource constrained devices
Artificial intelligence (AI) is changing people's everyday life. AI techniques such as Deep Neural Networks (DNN) rely on heavy computational models, which are in principle designed to be executed on powerful HW platforms, such as desktop or server environments. However, the increasing need to apply such solutions in people's everyday life has encouraged the research for methods to allow their deployment on embedded, portable and stand-alone devices, such as mobile phones, which exhibit relatively low memory and computational resources. Such methods targets both the development of lightweight AI algorithms and their acceleration through dedicated HW.
This thesis focuses on the development of lightweight AI solutions, with attention to deep neural networks, to facilitate their deployment on resource constrained devices. Focusing on the computer vision field, we show how putting together the self learning ability of deep neural networks with application-specific knowledge, in the form of feature engineering, it is possible to dramatically reduce the total memory and computational burden, thus allowing the deployment on edge devices. The proposed approach aims to be complementary to already existing application-independent network compression solutions. In this work three main DNN optimization goals have been considered: increasing speed and accuracy, allowing training at the edge, and allowing execution on a microcontroller. For each of these we deployed the resulting algorithm to the target embedded device and measured its performance
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