Static Human Detection and Scenario Recognition via Wearable Thermal Sensing System

Conventional wearable sensors are mainly used to detect the physiological and activity information of individuals who wear them, but fail to perceive the information of the surrounding environment. This paper presents a wearable thermal sensing system to detect and perceive the information of surrounding human subjects. The proposed system is developed based on a pyroelectric infrared sensor. Such a sensor system aims to provide surrounding information to blind people and people with weak visual capability to help them adapt to the environment and avoid collision. In order to achieve this goal, a low-cost, low-data-throughput binary sampling and analyzing scheme is proposed. We also developed a conditioning sensing circuit with a low-noise signal amplifier and programmable system on chip (PSoC) to adjust the amplification gain. Three statistical features in information space are extracted to recognize static humans and human scenarios in indoor environments. The results demonstrate that the proposed wearable thermal sensing system and binary statistical analysis method are efficient in static human detection and human scenario perception

Sub-Nyquist Sampling: Bridging Theory and Practice

Sampling theory encompasses all aspects related to the conversion of continuous-time signals to discrete streams of numbers. The famous Shannon-Nyquist theorem has become a landmark in the development of digital signal processing. In modern applications, an increasingly number of functions is being pushed forward to sophisticated software algorithms, leaving only those delicate finely-tuned tasks for the circuit level. In this paper, we review sampling strategies which target reduction of the ADC rate below Nyquist. Our survey covers classic works from the early 50's of the previous century through recent publications from the past several years. The prime focus is bridging theory and practice, that is to pinpoint the potential of sub-Nyquist strategies to emerge from the math to the hardware. In that spirit, we integrate contemporary theoretical viewpoints, which study signal modeling in a union of subspaces, together with a taste of practical aspects, namely how the avant-garde modalities boil down to concrete signal processing systems. Our hope is that this presentation style will attract the interest of both researchers and engineers in the hope of promoting the sub-Nyquist premise into practical applications, and encouraging further research into this exciting new frontier.Comment: 48 pages, 18 figures, to appear in IEEE Signal Processing Magazin

Compressed sensing for enhanced through-the-wall radar imaging

Through-the-wall radar imaging (TWRI) is an emerging technology that aims to capture scenes behind walls and other visually opaque materials. The abilities to sense through walls are highly desirable for both military and civil applications, such as search and rescue missions, surveillance, and reconnaissance. TWRI systems, however, face with several challenges including prolonged data acquisition, large objects, strong wall clutter, and shadowing effects, which limit the radar imaging performances and hinder target detection and localization

An assisting model for the visually challenged to detect bus door accurately

Visually impaired individuals are increasing and as per global statistics, around 39 million are blind, and 246 million are affected by low vision. Even in India, as per the recent reviews, over 5 million visually challenged people are present. Authors performed a survey of some critical problems the visually challenged people faced in India from the centre for visually challenged (CVC) School established by UVSM Hospitals. Among the major problems identified through survey, most of these persons prefer carrying out their tasks independently, and depend on public transport buses for migration. However, critical sub-problems being faced include; bus door identification and identifying the bus route number accurately. This article aims to provide solutions in helping visually challenged individuals to identify exact bus that drives them to their destination, its door, bus number, and the path for boarding bus. A video sequence of current scenario would be sent to mobile, in which the actual processing of image is carried out. After the video sequence processing, generated output is a voice message that specifies the bus's location, door, and exact information of the bus number along the road path directly to the user using a wireless device aiming foa a low-cost solution

Recent Developments in Video Surveillance

With surveillance cameras installed everywhere and continuously streaming thousands of hours of video, how can that huge amount of data be analyzed or even be useful? Is it possible to search those countless hours of videos for subjects or events of interest? Shouldn’t the presence of a car stopped at a railroad crossing trigger an alarm system to prevent a potential accident? In the chapters selected for this book, experts in video surveillance provide answers to these questions and other interesting problems, skillfully blending research experience with practical real life applications. Academic researchers will find a reliable compilation of relevant literature in addition to pointers to current advances in the field. Industry practitioners will find useful hints about state-of-the-art applications. The book also provides directions for open problems where further advances can be pursued

Reasoning about Scene and Image Structure for Computer Vision

The wide availability of cheap consumer cameras has democratized photography for novices and experts alike, with more than a trillion photographs taken each year. While many of these cameras---especially those on mobile phones---have inexpensive optics and make imperfect measurements, the use of modern computational techniques can allow the recovery of high-quality photographs as well as of scene attributes. In this dissertation, we explore algorithms to infer a wide variety of physical and visual properties of the world, including color, geometry, reflectance etc., from images taken by casual photographers in unconstrained settings. We specifically focus on neural network-based methods, while incorporating domain knowledge about scene structure and the physics of image formation. We describe novel techniques to produce high-quality images in poor lighting environments, train scene map estimators in the absence of ground-truth data and learn to output our understanding and uncertainty on the scene given observed images. The key to inferring scene properties from casual photography is to exploit the internal structure of natural scenes and the expressive capacity of neural networks. We demonstrate that neural networks can be used to identify the internal structure of scenes maps, and that our prior understanding on natural scenes can shape the design, training and the output representation of neural networks