10,208 research outputs found
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
Reduced Memory Region Based Deep Convolutional Neural Network Detection
Accurate pedestrian detection has a primary role in automotive safety: for
example, by issuing warnings to the driver or acting actively on car's brakes,
it helps decreasing the probability of injuries and human fatalities. In order
to achieve very high accuracy, recent pedestrian detectors have been based on
Convolutional Neural Networks (CNN). Unfortunately, such approaches require
vast amounts of computational power and memory, preventing efficient
implementations on embedded systems. This work proposes a CNN-based detector,
adapting a general-purpose convolutional network to the task at hand. By
thoroughly analyzing and optimizing each step of the detection pipeline, we
develop an architecture that outperforms methods based on traditional image
features and achieves an accuracy close to the state-of-the-art while having
low computational complexity. Furthermore, the model is compressed in order to
fit the tight constrains of low power devices with a limited amount of embedded
memory available. This paper makes two main contributions: (1) it proves that a
region based deep neural network can be finely tuned to achieve adequate
accuracy for pedestrian detection (2) it achieves a very low memory usage
without reducing detection accuracy on the Caltech Pedestrian dataset.Comment: IEEE 2016 ICCE-Berli
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
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
Unsupervised Network Pretraining via Encoding Human Design
Over the years, computer vision researchers have spent an immense amount of
effort on designing image features for the visual object recognition task. We
propose to incorporate this valuable experience to guide the task of training
deep neural networks. Our idea is to pretrain the network through the task of
replicating the process of hand-designed feature extraction. By learning to
replicate the process, the neural network integrates previous research
knowledge and learns to model visual objects in a way similar to the
hand-designed features. In the succeeding finetuning step, it further learns
object-specific representations from labeled data and this boosts its
classification power. We pretrain two convolutional neural networks where one
replicates the process of histogram of oriented gradients feature extraction,
and the other replicates the process of region covariance feature extraction.
After finetuning, we achieve substantially better performance than the baseline
methods.Comment: 9 pages, 11 figures, WACV 2016: IEEE Conference on Applications of
Computer Visio
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