17,442 research outputs found
Pedestrian classification on transfer learning based deep convolutional neural network for partial occlusion handling
The investigation of a deep neural network for pedestrian classification using transfer learning methods is proposed in this study. The development of deep convolutional neural networks has significantly improved the autonomous driver assistance system for pedestrian classification. However, the presence of partially occluded parts and the appearance variation under complex scenes are still robust to challenge in the pedestrian detection system. To address this problem, we proposed six transfer learning models: end-to-end convolutional neural network (CNN) model, scratch-trained residual network (ResNet50) model, and four transfer learning models: visual geometry group 16 (VGG16), GoogLeNet (InceptionV3), ResNet50, and MobileNet. The performance of the pedestrian classification was evaluated using four publicly datasets: Institut National de Recherche en Sciences et Technologies du Numérique (INRIA), Prince of Songkla University (PSU), CVC05, and Walailak University (WU) datasets. The experimental results show that six transfer learning models achieve classification accuracy of 65.2% (end-to-end CNN), 92.92% (scratch-trained ResNet50), 97.15% (pre-trained VGG16), 94.39% (pre-trained InceptionV3), 90.43% (pre-trained ResNet50), and 98.69% (pre-trained MobileNet) using data from Southern Thailand (PSU dataset). Further analysis reveals that the deeper the ConvNet architecture, the more specific information of features is provided. In addition, the deep ConvNet architecture can distinguish pedestrian occluded patterns while being trained with partially occluded parts of data samples
Perceptual Generative Adversarial Networks for Small Object Detection
Detecting small objects is notoriously challenging due to their low
resolution and noisy representation. Existing object detection pipelines
usually detect small objects through learning representations of all the
objects at multiple scales. However, the performance gain of such ad hoc
architectures is usually limited to pay off the computational cost. In this
work, we address the small object detection problem by developing a single
architecture that internally lifts representations of small objects to
"super-resolved" ones, achieving similar characteristics as large objects and
thus more discriminative for detection. For this purpose, we propose a new
Perceptual Generative Adversarial Network (Perceptual GAN) model that improves
small object detection through narrowing representation difference of small
objects from the large ones. Specifically, its generator learns to transfer
perceived poor representations of the small objects to super-resolved ones that
are similar enough to real large objects to fool a competing discriminator.
Meanwhile its discriminator competes with the generator to identify the
generated representation and imposes an additional perceptual requirement -
generated representations of small objects must be beneficial for detection
purpose - on the generator. Extensive evaluations on the challenging
Tsinghua-Tencent 100K and the Caltech benchmark well demonstrate the
superiority of Perceptual GAN in detecting small objects, including traffic
signs and pedestrians, over well-established state-of-the-arts
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
Comparing Computing Platforms for Deep Learning on a Humanoid Robot
The goal of this study is to test two different computing platforms with
respect to their suitability for running deep networks as part of a humanoid
robot software system. One of the platforms is the CPU-centered Intel NUC7i7BNH
and the other is a NVIDIA Jetson TX2 system that puts more emphasis on GPU
processing. The experiments addressed a number of benchmarking tasks including
pedestrian detection using deep neural networks. Some of the results were
unexpected but demonstrate that platforms exhibit both advantages and
disadvantages when taking computational performance and electrical power
requirements of such a system into account.Comment: 12 pages, 5 figure
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