3,646 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
Learning to count with deep object features
Learning to count is a learning strategy that has been recently proposed in
the literature for dealing with problems where estimating the number of object
instances in a scene is the final objective. In this framework, the task of
learning to detect and localize individual object instances is seen as a harder
task that can be evaded by casting the problem as that of computing a
regression value from hand-crafted image features. In this paper we explore the
features that are learned when training a counting convolutional neural network
in order to understand their underlying representation. To this end we define a
counting problem for MNIST data and show that the internal representation of
the network is able to classify digits in spite of the fact that no direct
supervision was provided for them during training. We also present preliminary
results about a deep network that is able to count the number of pedestrians in
a scene.Comment: This paper has been accepted at Deep Vision Workshop at CVPR 201
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
Effective fusion of complementary information captured by multi-modal sensors
(visible and infrared cameras) enables robust pedestrian detection under
various surveillance situations (e.g. daytime and nighttime). In this paper, we
present a novel box-level segmentation supervised learning framework for
accurate and real-time multispectral pedestrian detection by incorporating
features extracted in visible and infrared channels. Specifically, our method
takes pairs of aligned visible and infrared images with easily obtained
bounding box annotations as input and estimates accurate prediction maps to
highlight the existence of pedestrians. It offers two major advantages over the
existing anchor box based multispectral detection methods. Firstly, it
overcomes the hyperparameter setting problem occurred during the training phase
of anchor box based detectors and can obtain more accurate detection results,
especially for small and occluded pedestrian instances. Secondly, it is capable
of generating accurate detection results using small-size input images, leading
to improvement of computational efficiency for real-time autonomous driving
applications. Experimental results on KAIST multispectral dataset show that our
proposed method outperforms state-of-the-art approaches in terms of both
accuracy and speed
Group-level Emotion Recognition using Transfer Learning from Face Identification
In this paper, we describe our algorithmic approach, which was used for
submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017)
group-level emotion recognition sub-challenge. We extracted feature vectors of
detected faces using the Convolutional Neural Network trained for face
identification task, rather than traditional pre-training on emotion
recognition problems. In the final pipeline an ensemble of Random Forest
classifiers was learned to predict emotion score using available training set.
In case when the faces have not been detected, one member of our ensemble
extracts features from the whole image. During our experimental study, the
proposed approach showed the lowest error rate when compared to other explored
techniques. In particular, we achieved 75.4% accuracy on the validation data,
which is 20% higher than the handcrafted feature-based baseline. The source
code using Keras framework is publicly available.Comment: 5 pages, 3 figures, accepted for publication at ICMI17 (EmotiW Grand
Challenge
- …