825 research outputs found
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
Object Recognition from very few Training Examples for Enhancing Bicycle Maps
In recent years, data-driven methods have shown great success for extracting
information about the infrastructure in urban areas. These algorithms are
usually trained on large datasets consisting of thousands or millions of
labeled training examples. While large datasets have been published regarding
cars, for cyclists very few labeled data is available although appearance,
point of view, and positioning of even relevant objects differ. Unfortunately,
labeling data is costly and requires a huge amount of work. In this paper, we
thus address the problem of learning with very few labels. The aim is to
recognize particular traffic signs in crowdsourced data to collect information
which is of interest to cyclists. We propose a system for object recognition
that is trained with only 15 examples per class on average. To achieve this, we
combine the advantages of convolutional neural networks and random forests to
learn a patch-wise classifier. In the next step, we map the random forest to a
neural network and transform the classifier to a fully convolutional network.
Thereby, the processing of full images is significantly accelerated and
bounding boxes can be predicted. Finally, we integrate data of the Global
Positioning System (GPS) to localize the predictions on the map. In comparison
to Faster R-CNN and other networks for object recognition or algorithms for
transfer learning, we considerably reduce the required amount of labeled data.
We demonstrate good performance on the recognition of traffic signs for
cyclists as well as their localization in maps.Comment: Submitted to IV 2018. This research was supported by German Research
Foundation DFG within Priority Research Programme 1894 "Volunteered
Geographic Information: Interpretation, Visualization and Social Computing
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
Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
[EN] Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) Grant funded by the Korea Government (MSIT) (2019-0-00136, Development of AI-Convergence Technologies for Smart City Industry Productivity Innovation); The work of Javier Del Ser was supported by the Basque Government through the EMAITEK and ELKARTEK Programs, as well as by the Department of Education of this institution (Consolidated Research Group MATHMODE, IT1294-19); VHCA received support from the Brazilian National Council for Research and Development (CNPq, Grant #304315/2017-6 and #430274/2018-1).Muhammad, K.; Ullah, A.; Lloret, J.; Del Ser, J.; De Albuquerque, VHC. (2021). Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions. IEEE Transactions on Intelligent Transportation Systems. 22(7):4316-4336. https://doi.org/10.1109/TITS.2020.30322274316433622
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