Computer Vision in the Age of IoT
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Computer vision and pattern recognition algorithms are important for IoT applications like smart homes and healthcare that involve large camera networks. Academic expertise is needed for accuracy and efficiency, while industrial concerns focus on system integration, configuration and management. The presentation describes a large-scale video surveillance system using heterogeneous information fusion and visualization across a university campus. It also discusses implementing system self-awareness through fault, environment and context awareness, and presents methods for real-time camera anomaly detection.
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This document discusses parallelizing computer vision algorithms using GPGPU computing. It begins with an introduction to multicore computing and GPUs. It explains that as CPU clock speeds can no longer increase due to power constraints, the industry has shifted to multicore CPUs and GPUs to continue improving performance. Computer vision algorithms are well-suited to parallelization on GPUs due to their massive data processing needs. The document reviews GPU architectures from Nvidia, Qualcomm, AMD, and ARM that can be used to accelerate computer vision. It also discusses parallel programming frameworks for GPUs like CUDA, OpenCL, and OpenACC.Towards Embedded Computer Vision - New @ 2013
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This document discusses parallel computing with GPUs and CUDA. It begins by explaining that the multicore era requires parallel computing approaches. It then provides an overview of GPU architecture and programming with CUDA. Specific examples of using GPUs for image restoration, feature extraction, and video processing are mentioned.Markov Random Field (MRF)
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1. It is explained from the View of Probabilistic Graphical Models.
2. It is a SUPPLEMENTS FOR
Bayesian Networks Course.
07 approximate inference in bn
07 approximate inference in bnIEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing
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This document provides an overview of Bayesian networks and probabilistic graphical models (PGMs). It outlines the goals of learning how to build graphical models using graph theory and perform inference under uncertainty using probability theory. It also lists some example PGM models like Markov random fields, hidden Markov models, dynamic Bayesian networks, naive Bayes models, and applications in computer vision. Finally, it provides the table of contents and references for further self-study on PGMs and Bayesian networks.04 Uncertainty inference(continuous)
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This document provides an introduction to probability and probability distributions for Bayesian networks. It begins with a review of basic probability concepts like events, axioms of probability, and theorems derived from the axioms. It then discusses random variables, including discrete, continuous, and random vector variables. Examples of random variables in image processing and computer vision are provided. The document concludes with an overview of probability distributions as a set of probabilities assigned to a random variable or vector.02 Statistics review
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Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
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help of Advanced technologies like Remote Sensing and Geographic Information Systems is
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Changes in vegetation cover refer to variations in the distribution, composition, and overall
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Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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- 1. COMPUTER VISION IN THE AGE OF IOT FROM BIG DATA TO SYSTEM SELF-AWARENESS YUAN-KAI WANG FU-JEN CATHOLIC UNIVERSITY 2014/8/26 Plenary speech in International Conference on Pattern Recognition, Stockholm, Sweden.1
- 2. 2 Healthcare Smart HomeEntertainment Security Transportation Industrial Others Internet of Things Retails
- 3. 3
- 4. IoC Challenges 4 • IoC is closely related to camera networks, which becomes a large-scale system. • System-level thinking is necessary • Academic expertise on algorithms, such as computer vision and pattern recognition, are important for accuracy, efficiency and convenience. • Industrial concerns on system integration are critical for configuration, maintenance and management. • Autonomic computing with self-awareness • Paul Horn, 2001. • Camera collaboration of anomaly events
- 5. Our Experience Wide-Area Video Surveillance in Campus 5
- 6. Video Surveillance Cloud 6 Intrusion detection PTZ pedestrian tracking Camera anomaly detection Video Synopsis Geographical visualizationMulti-resolution fusion and visualization Parking lots estimation Illegal parking detection Video based face extraction Storage Area Network PC Mobile device Multi-core Hypervisor GPGPU Yuan-Kai Wang et al. "A Large Scale Video Surveillance System with Heterogeneous Information Fusion and Visualization for Wide Area Monitoring," IEEE Int. Conf. IIHMSP, 2012.
- 8. System Self-Awareness 8 Fault awareness Environment awareness Context awareness
- 9. Camera Anomaly Detection: Self-awareness of Camera 9 Yuan-Kai Wang et al. "Real-Time Camera Anomaly Detection for Real-World Video Surveillance," ICMLC, 2011. Normal Anomaly! Background updating graph cut Online Kalman filter finite state machine Features of image quality and replacement
- 10. Traffic Camera Anomaly Detection Yuan-Kai Wang et al. "Traffic Camera Anomaly Detection," ICPR 2014. (8/28, Balcony Level5, PM 4:00-5:40, paper no. : ThCT4p.26) 10
- 11. 11
- 12. Related Publications " "A Large Scale Video Surveillance System with Heterogeneous Information Fusion and Visualization for Wide Area Monitoring," IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2012. " "Real-Time Camera Anomaly Detection for Real-World Video Surveillance," International Conference on Machine Learning and Cybernetics, 2011. " "Traffic Camera Anomaly Detection," ICPR 2014. (8/28, Balcony Level5, PM 4:00-5:40, paper no. : ThCT4p.26) 12