109,582 research outputs found
The Visual Social Distancing Problem
One of the main and most effective measures to contain the recent viral
outbreak is the maintenance of the so-called Social Distancing (SD). To comply
with this constraint, workplaces, public institutions, transports and schools
will likely adopt restrictions over the minimum inter-personal distance between
people. Given this actual scenario, it is crucial to massively measure the
compliance to such physical constraint in our life, in order to figure out the
reasons of the possible breaks of such distance limitations, and understand if
this implies a possible threat given the scene context. All of this, complying
with privacy policies and making the measurement acceptable. To this end, we
introduce the Visual Social Distancing (VSD) problem, defined as the automatic
estimation of the inter-personal distance from an image, and the
characterization of the related people aggregations. VSD is pivotal for a
non-invasive analysis to whether people comply with the SD restriction, and to
provide statistics about the level of safety of specific areas whenever this
constraint is violated. We then discuss how VSD relates with previous
literature in Social Signal Processing and indicate which existing Computer
Vision methods can be used to manage such problem. We conclude with future
challenges related to the effectiveness of VSD systems, ethical implications
and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this
manuscript and they are listed by alphabetical order. Under submissio
Traffic monitoring using image processing : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Telecommunications Engineering at Massey University, Palmerston North, New Zealand
Traffic monitoring involves the collection of data describing the characteristics of vehicles and their movements. Such data may be used for automatic tolls, congestion and incident detection, law enforcement, and road capacity planning etc. With the recent advances in Computer Vision technology, videos can be analysed automatically and relevant information can be extracted for particular applications. Automatic surveillance using video cameras with image processing technique is becoming a powerful and useful technology for traffic monitoring. In this research project, a video image processing system that has the potential to be developed for real-time application is developed for traffic monitoring including vehicle tracking, counting, and classification. A heuristic approach is applied in developing this system. The system is divided into several parts, and several different functional components have been built and tested using some traffic video sequences. Evaluations are carried out to show that this system is robust and can be developed towards real-time applications
TasselNet: Counting maize tassels in the wild via local counts regression network
Accurately counting maize tassels is important for monitoring the growth
status of maize plants. This tedious task, however, is still mainly done by
manual efforts. In the context of modern plant phenotyping, automating this
task is required to meet the need of large-scale analysis of genotype and
phenotype. In recent years, computer vision technologies have experienced a
significant breakthrough due to the emergence of large-scale datasets and
increased computational resources. Naturally image-based approaches have also
received much attention in plant-related studies. Yet a fact is that most
image-based systems for plant phenotyping are deployed under controlled
laboratory environment. When transferring the application scenario to
unconstrained in-field conditions, intrinsic and extrinsic variations in the
wild pose great challenges for accurate counting of maize tassels, which goes
beyond the ability of conventional image processing techniques. This calls for
further robust computer vision approaches to address in-field variations. This
paper studies the in-field counting problem of maize tassels. To our knowledge,
this is the first time that a plant-related counting problem is considered
using computer vision technologies under unconstrained field-based environment.Comment: 14 page
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