Performance profiling in sports allow evaluating opponents' tactics and the development of counter tactics to gain a competitive advantage. the work presented develops a comprehensive methodology to automate tacti...
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ISBN:
(纸本)9781538607336
Performance profiling in sports allow evaluating opponents' tactics and the development of counter tactics to gain a competitive advantage. the work presented develops a comprehensive methodology to automate tactical profiling in elite badminton. the proposed approach uses computervision techniques to automate data gathering from video footage. the image processing algorithm is validated using video footage of the highest level tournaments, including the Olympic Games. the average accuracy of player position detection is 96.03% and 97.09% on the two halves of a badminton court. Next, frequent trajectories of badminton players are extracted and classified according to their tactical relevance. the classification performs at 97.79% accuracy, 97.81% precision, 97.44% recall, and 97.62% F-score. the combination of automated player position detection, frequent trajectory extraction, and the subsequent classification can be used to automatically generate player tactical profiles.
In this paper, a novel steganography algorithm based on an improved "Matrix pattern" (MP) method is presented. In this process, firstly, an RGB image is divided into the non-overlapping square-sized blocks. ...
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ISBN:
(纸本)9781538607336
In this paper, a novel steganography algorithm based on an improved "Matrix pattern" (MP) method is presented. In this process, firstly, an RGB image is divided into the non-overlapping square-sized blocks. Next, 95 dynamic-sized unique matrix patterns are automatically generated using the 4th and 5th bit layers of the green layer of each block, which are assigned to 95 English keyboard characters. then, the blue layer of each block is used for embedding secret messages by adding matrix patterns which are assigned to the characters of the secret message. the results show that this algorithm has a high resistance against steganalysis attacks, including Regular Singular (RS), Sample Pair (SP), and PVD based attacks. Furthermore, the proposed algorithm not only improves capacity by over 27% when compared to the existing method, but also results in a slightly better transparency of the stego-image.
Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computervision, thanks to recently introduced 3D sensors. In the literature, naive methods simply ...
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ISBN:
(纸本)9781538607336
Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computervision, thanks to recently introduced 3D sensors. In the literature, naive methods simply transfer off-the-shelf techniques from video to the skeletal representation. However, the current state-of-the-art is contended between to different paradigms: kernel-based methods and feature learning with (recurrent) neural networks. Both approaches show strong performances, yet they exhibit heavy, but complementary, drawbacks. Motivated by this fact, our work aims at combining together the best of the two paradigms, by proposing an approach where a shallow network is fed with a covariance representation. Our intuition is that, as long as the dynamics is effectively modeled, there is no need for the classification network to be deep nor recurrent in order to score favorably. We validate this hypothesis in a broad experimental analysis over 6 publicly available datasets.
this paper introduces a novel large dataset for example-based single image super-resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. the challenge is the first challenge of its kind, ...
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ISBN:
(纸本)9781538607336
this paper introduces a novel large dataset for example-based single image super-resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. the challenge is the first challenge of its kind, with 6 competitions, hundreds of participants and tens of proposed solutions. Our newly collected DIVerse 2K resolution image dataset (DIV2K) was employed by the challenge. In our study we compare the solutions from the challenge to a set of representative methods from the literature and evaluate them using diverse measures on our proposed DIV2K dataset. Moreover, we conduct a number of experiments and draw conclusions on several topics of interest. We conclude that the NTIRE 2017 challenge pushes the state-of-the-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on our newly proposed DIV2K.
this paper indicates the dataset and challenges evaluated under PETS2017. In this edition PETS continues the evaluation theme of on-board surveillance systems for protection of mobile critical assets as set in PETS 20...
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ISBN:
(纸本)9781538607336
this paper indicates the dataset and challenges evaluated under PETS2017. In this edition PETS continues the evaluation theme of on-board surveillance systems for protection of mobile critical assets as set in PETS 2016. the datasets include (1) the ARENA Dataset;an RGB camera dataset, as used for PETS2014 to PETS 2016, which addresses protection of trucks;and (2) the IPATCH Dataset;a multi sensor dataset, as used in PETS2016, addressing the application of multi sensor surveillance to protect a vessel at sea from piracy. the datasets allow for performance evaluation of tracking in low-density scenarios and detection of various surveillance events ranging from innocuous abnormalities to dangerous and criminal situations. Training data for tracking algorithms is released withthe dataset;tracking data is also available for authors addressing only surveillance event detection challenges but not working on tracking.
the use of surveillance cameras continues to increase, ranging from conventional applications such as law enforcement to newer scenarios with looser requirements such as gathering business intelligence. Humans still p...
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ISBN:
(纸本)9781538607336
the use of surveillance cameras continues to increase, ranging from conventional applications such as law enforcement to newer scenarios with looser requirements such as gathering business intelligence. Humans still play an integral part in using and interpreting the footage from these systems, but are also a significant factor in causing unintentional privacy breaches. As computervision methods continue to improve, we argue in this position paper that system designers should reconsider the role of machines in surveillance, and how automation can be used to help protect privacy. We explore this by discussing the impact of the human-in-the-loop, the potential for using abstraction and distributed computing to further privacy goals, and an approach for determining when video footage should be hidden from human users. We propose that in an ideal surveillance scenario, a privacy-affirming framework causes collected camera footage to be processed by computers directly, and never shown to humans. this implicitly requires humans to establish trust, to believe that computervision systems can generate sufficiently accurate results without human supervision, so that if information about people must be gathered, unintentional data collection is mitigated as much as possible.
Onboard monocular cameras have been widely deployed in both public transit and personal vehicles. Obtaining vehicle-pedestrian near-miss event data from onboard monocular vision systems may be cost-effective compared ...
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ISBN:
(纸本)9781538607336
Onboard monocular cameras have been widely deployed in both public transit and personal vehicles. Obtaining vehicle-pedestrian near-miss event data from onboard monocular vision systems may be cost-effective compared with onboard multiple-sensor systems or traffic surveillance videos. But extracting near-misses from onboard monocular vision is challenging and little work has been published. this paper fills the gap by developing a framework to automatically detect vehicle-pedestrian near-misses through onboard monocular vision. the proposed framework can estimate depth and real-world motion information through monocular vision with a moving video background. the experimental results based on processing over 30-hours video data demonstrate the ability of the system to capture near-misses by comparison withthe events logged by the Rosco/MobilEye Shield+ system which includes four cameras working cooperatively. the detection overlap rate reaches over 90% withthe thresholds properly set.
In this paper, we look at the societal effects of computervision technologies from the perspective of the future minds in computervision: senior year engineering students. Engineering education has traditionally foc...
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ISBN:
(纸本)9781538607336
In this paper, we look at the societal effects of computervision technologies from the perspective of the future minds in computervision: senior year engineering students. Engineering education has traditionally focused on technical skills and knowledge. Nowadays, the need for educating engineers in socio-technical skills and reflective thinking, especially on the bright and dark sides of the technology they develop, is being recognized. We advocate for the integration of social awareness modules into computervision courses so that the societal effects of technology can be studied together withthe technology itself, as opposed to the often more generic 'impact of technology on society' courses. Such modules provide a venue for students to reflect on the real-world consequences of technology in concrete, practical contexts. In this paper, we present qualitative results of an observational study analyzing essays of senior year engineering students, who wrote about societal impacts of computervision technologies of their choice. Privacy and security issues ranked as the top impact topics discussed by students among 50 topics. Similar social awareness modules would apply well to other advanced technical courses of the engineering curriculum where privacy and security are a major concern, such as big data courses. We believe that such modules are highly likely to enhance the reflective abilities of engineering graduates regarding societal impacts of novel technologies.
Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an app...
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ISBN:
(纸本)9781538607336
Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an appearance-based method that, in contrast to a long-standing line of work in computervision, only takes the full face image as input. Our method encodes the face image using a convolutional neural network with spatial weights applied on the feature maps to flexibly suppress or enhance information in different facial regions. through extensive evaluation, we show that our full-face method significantly outperforms the state of the art for both 2D and 3D gaze estimation, achieving improvements of up to 14.3% on MPIIGaze and 27.7% on EYEDIAP for person-independent 3D gaze estimation. We further show that this improvement is consistent across different illumination conditions and gaze directions and particularly pronounced for the most challenging extreme head poses.
computervision based technologies have seen widespread adoption over the recent years. this use is not limited to the rapid adoption of facial recognition technology but extends to facial expression recognition, scen...
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ISBN:
(纸本)9781538607336
computervision based technologies have seen widespread adoption over the recent years. this use is not limited to the rapid adoption of facial recognition technology but extends to facial expression recognition, scene recognition and more. these developments raise privacy concerns and call for novel solutions to ensure adequate user awareness, and ideally, control over the resulting collection and use of potentially sensitive data. While cameras have become ubiquitous, most of the time users are not even aware of their presence. In this paper we introduce a novel distributed privacy infrastructure for the Internet-of-things and discuss in particular how it can help enhance user's awareness of and control over the collection and use of video data about them. the infrastructure, which has undergone early deployment and evaluation on two campuses, supports the automated discovery of IoT resources and the selective notification of users. this includes the presence of computervision applications that collect data about users. In particular, we describe an implementation of functionality that helps users discover nearby cameras and choose whether or not they want their faces to be denatured in the video streams.
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