17 research outputs found
Intelligent Sensors for Human Motion Analysis
The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems
Area-wide Integrated Pest Management
Over 98% of sprayed insecticides and 95% of herbicides reach a destination other than their target species, including non-target species, air, water and soil. The extensive reliance on insecticide use reduces biodiversity, contributes to pollinator decline, destroys habitat, and threatens endangered species. This book offers a more effective application of the Integrated Pest Management (IPM) approach, on an area-wide (AW) or population-wide (AW-IPM) basis, which aims at the management of the total population of a pest, involving a coordinated effort over often larger areas. For major livestock pests, vectors of human diseases and pests of high-value crops with low pest tolerance, there are compelling economic reasons for participating in AW-IPM. This new textbook attempts to address various fundamental components of AW-IPM, e.g. the importance of relevant problem-solving research, the need for planning and essential baseline data collection, the significance of integrating adequate tools for appropriate control strategies, and the value of pilot trials, etc. With chapters authored by 184 experts from more than 31 countries, the book includes many technical advances in the areas of genetics, molecular biology, microbiology, resistance management, and social sciences that facilitate the planning and implementing of area-wide strategies. The book is essential reading for the academic and applied research community as well as national and regional government plant and human/animal health authorities with responsibility for protecting plant and human/animal health
IV Міжнародний науковий конгрес "Society of Ambient Intelligence - 2021" (ISCSAI 2021). Кривий Ріг, Україна, 12-16 квітня 2021 року
IV Міжнародний науковий конгрес "Society of Ambient Intelligence - 2021" (ISCSAI 2021). Кривий Ріг, Україна, 12-16 квітня 2021 року - матеріали.IV International Scientific Congress “Society of Ambient Intelligence – 2021” (ISCSAI 2021). Kryvyi Rih, Ukraine, April 12-16, 2021 - proceedings
自己および相互オクルージョンを考慮したマルチタスク深層学習による人物スケルトン推定
学位の種別: 修士University of Tokyo(東京大学
FROM VISUAL SALIENCY TO VIDEO BEHAVIOUR UNDERSTANDING
In a world of ever increasing amounts of video data, we are forced to abandon traditional
methods of scene interpretation by fully manual means. Under such circumstances, some form
of automation is highly desirable but this can be a very open ended issue with high complexity.
Dealing with such large amounts of data is a non-trivial task that requires efficient selective
extraction of parts of a scene which have the potential to develop a higher semantic meaning,
alone, or in combination with others. In particular, the types of video data that are in
need of automated analysis tend to be outdoor scenes with high levels of activity generated
from either foreground or background. Such dynamic scenes add considerable complexity
to the problem since we cannot rely on motion energy alone to detect regions of interest.
Furthermore, the behaviour of these regions of motion can differ greatly, while still being
highly dependent, both spatially and temporally on the movement of other objects within
the scene. Modelling these dependencies, whilst eliminating as much redundancy from the
feature extraction process as possible are the challenges addressed by this thesis.
In the first half, finding the right mechanism to extract and represent meaningful features
from dynamic scenes with no prior knowledge is investigated. Meaningful or salient information
is treated as the parts of a scene that stand out or seem unusual or interesting to
us. The novelty of the work is that it is able to select salient scales in both space and time
in which a particular spatio-temporal volume is considered interesting relative to the rest of
the scene. By quantifying the temporal saliency values of regions of motion, it is possible to
consider their importance in terms of both the long and short-term. Variations in entropy
over spatio-temporal scales are used to select a context dependent measure of the local scene
dynamics. A method of quantifying temporal saliency is devised based on the variation of
the entropy of the intensity distribution in a spatio-temporal volume over incraeasing scales.
Entropy is used over traditional filter methods since the stability or predictability of the intensity
distribution over scales of a local spatio-temporal region can be defined more robustly
relative to the context of its neighbourhood, even for regions exhibiting high intensity variation
due to being extremely textured. Results show that it is possible to extract both locally
salient features as well as globally salient temporal features from contrasting scenerios.
In the second part of the thesis, focus will shift towards binding these spatio-temporally
salient features together so that some semantic meaning can be inferred from their interaction.
Interaction in this sense, refers to any form of temporally correlated behaviour between
any salient regions of motion in a scene. Feature binding as a mechanism for interactive
behaviour understanding is particularly important if we consider that regions of interest may
not be treated as particularly significant individually, but represent much more semantically
when considered in combination. Temporally correlated behaviour is identified and classified
using accumulated co-occurrences of salient features at two levels. Firstly, co-occurrences are
accumulated for spatio-temporally proximate salient features to form a local representation.
Then, at the next level, the co-occurrence of these locally spatio-temporally bound features
are accumulated again in order to discover unusual behaviour in the scene. The novelty of
this work is that there are no assumptions made about whether interacting regions should be
spatially proximate. Furthermore, no prior knowledge of the scene topology is used. Results
show that it is possible to detect unusual interactions between regions of motion, which can
visually infer higher levels of semantics.
In the final part of the thesis, a more specific investigation of human behaviour is addressed
through classification and detection of interactions between 2 human subjects. Here, further
modifications are made to the feature extraction process in order to quantify the spatiotemporal
saliency of a region of motion. These features are then grouped to find the people
in the scene. Then, a loose pose distribution model is extracted for each person for finding
salient correlations between poses of two interacting people using canonical correlation
analysis. These canonical factors can be formed into trajectories and used for classification.
Levenshtein distance is then used to categorise the features. The novelty of the work is that
the interactions do not have to be spatially connected or proximate for them to be recognised.
Furthermore, the data used is outdoors and cluttered with non-stationary background. Results
show that co-occurrence techniques have the potential to provide a more generalised,
compact, and meaningful representation of dynamic interactive scene behaviour.EPRSC, part-funded by QinetiQ Ltd and a travel grant was also contributed by RAEng
Human behavior analysis in video surveillance: A Social Signal Processing perspective
The analysis of human activities is one of the most intriguing and important open issues for the automated video surveillance community. Since few years ago, it has been handled following a mere Computer Vision and Pattern Recognition perspective, where an activity corresponded to a temporal sequence of explicit actions (run, stop, sit, walk, etc.). Even under this simplistic assumption, the issue is hard, due to the strong diversity of the people appearance, the number of individuals considered (we may monitor single individuals, groups, crowd), the variability of the environmental conditions (indoor/outdoor, different weather conditions), and the kinds of sensors employed. More recently, the automated surveillance of human activities has been faced considering a new perspective, that brings in notions and principles from the social, affective, and psychological literature, and that is called Social Signal Processing (SSP). SSP employs primarily nonverbal cues, most of them are outside of conscious awareness, like face expressions and gazing, body posture and gestures, vocal characteristics, relative distances in the space and the like. This paper is the first review analyzing this new trend, proposing a structured snapshot of the state of the art and envisaging novel challenges in the surveillance domain where the cross-pollination of Computer Science technologies and Sociology theories may offer valid investigation strategies
Reports to the President
A compilation of annual reports for the 1988-1989 academic year, including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans