DETECTION AND LOCALISATION OF STATIONARY OBJECTS WITH A PAIR OF PTZ CAMERAS

2012 ◽  
Keyword(s):  
Stationary Objects

Moving Forward

2011 ◽  
Vol 42 (3) ◽  
pp. 225-230 ◽  
Author(s):  
Janet B. Ruscher
Keyword(s):  
Everyday Life ◽  
Interpersonal Communication ◽  
Spatial Metaphors ◽  
The People ◽  
Stationary Objects ◽  
Study Participants ◽  
Affective Forecasts

Two distinct spatial metaphors for the passage of time can produce disparate judgments about grieving. Under the object-moving metaphor, time seems to move past stationary people, like objects floating past people along a riverbank. Under the people-moving metaphor, time is stationary; people move through time as though they journey on a one-way street, past stationary objects. The people-moving metaphor should encourage the forecast of shorter grieving periods relative to the object-moving metaphor. In the present study, participants either received an object-moving or people-moving prime, then read a brief vignette about a mother whose young son died. Participants made affective forecasts about the mother’s grief intensity and duration, and provided open-ended inferences regarding a return to relative normalcy. Findings support predictions, and are discussed with respect to interpersonal communication and everyday life.

scholarly journals Collision Avoidance on Unmanned Aerial Vehicles Using Neural Network Pipelines and Flow Clustering Techniques

2021 ◽  
Vol 13 (13) ◽  
pp. 2643
Author(s):  
Dário Pedro ◽  
João P. Matos-Carvalho ◽  
José M. Fonseca ◽  
André Mora
Keyword(s):  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
High Demand ◽  
Novel Approach ◽  
Vision Sensors ◽  
Learning Techniques ◽  
Stationary Objects ◽  
Computationally Intensive ◽  
Unmanned Autonomous Vehicles ◽  
Positive Results

Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.

SFA-based ELM for remote detection of stationary objects

2021 ◽  
Author(s):  
Xiaolin Liang ◽  
Weifeng Zhu ◽  
Jiawen Sun ◽  
Jianqin Deng
Keyword(s):  
Remote Detection ◽  
Stationary Objects

Discrimination between stationary objects by the blind cave fishAnoptichthys jordani (Characidae)

1981 ◽  
Vol 143 (3) ◽  
pp. 375-381 ◽  
Author(s):  
R. Weissert ◽  
C. von Campenhausen
Keyword(s):  
Stationary Objects ◽  
Blind Cave

scholarly journals Real-Time Detection of Non-Stationary Objects Using Intensity Data in Automotive LiDAR SLAM

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6781
Author(s):  
Tomasz Nowak ◽  
Krzysztof Ćwian ◽  
Piotr Skrzypczyński
Keyword(s):  
Real Time ◽  
Network Models ◽  
Point Clouds ◽  
Intensity Data ◽  
Neural Network Models ◽  
Localization And Mapping ◽  
Stationary Objects ◽  
Intensity Images ◽  
Real Time Detection ◽  
Segmentation Task

This article aims at demonstrating the feasibility of modern deep learning techniques for the real-time detection of non-stationary objects in point clouds obtained from 3-D light detecting and ranging (LiDAR) sensors. The motion segmentation task is considered in the application context of automotive Simultaneous Localization and Mapping (SLAM), where we often need to distinguish between the static parts of the environment with respect to which we localize the vehicle, and non-stationary objects that should not be included in the map for localization. Non-stationary objects do not provide repeatable readouts, because they can be in motion, like vehicles and pedestrians, or because they do not have a rigid, stable surface, like trees and lawns. The proposed approach exploits images synthesized from the received intensity data yielded by the modern LiDARs along with the usual range measurements. We demonstrate that non-stationary objects can be detected using neural network models trained with 2-D grayscale images in the supervised or unsupervised training process. This concept makes it possible to alleviate the lack of large datasets of 3-D laser scans with point-wise annotations for non-stationary objects. The point clouds are filtered using the corresponding intensity images with labeled pixels. Finally, we demonstrate that the detection of non-stationary objects using our approach improves the localization results and map consistency in a laser-based SLAM system.

scholarly journals Classification of fish and non-fish acoustic tracks using discriminant function analysis

2012 ◽  
Vol 69 (2) ◽  
pp. 313-322 ◽  
Author(s):  
Yunbo Xie ◽  
Catherine G. J. Michielsens ◽  
Fiona J. Martens
Keyword(s):  
Discriminant Function ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Fish Populations ◽  
Fraser River ◽  
Discriminating Power ◽  
Sonar System ◽  
Stationary Objects ◽  
Individual Variables

Abstract Xie, Y., Michielsens, C. G. J., and Martens, F. J. 2012. Classification of fish and non-fish acoustic tracks using discriminant function analysis. – ICES Journal of Marine Science, 69: 313–322. Hydroacoustic data acquired for estimating fish populations contain information on both fish and non-fish targets, so sonar technicians traditionally rely on their knowledge of fish behaviour and experience with hydroacoustics to remove non-fish targets from the hydroacoustic data. This process is often labour-intensive and time-consuming, making real-time assessment of fish populations difficult. Simple solutions are not always available for all circumstances. However, the split-beam sonar data collected in the lower Fraser River, British Columbia, Canada, showed distinct signatures between actively swimming fish and non-fish objects such as drifting debris, surface bubbles, and stationary objects in the water column and off the river bottom. Acoustic tracks of fish and non-fish targets were characterized by differentiable statistical patterns that were amenable to discriminant function analysis (DFA). An application of DFA to segregate fish and non-fish targets detected by a split-beam sonar system in the lower Fraser River is presented, characteristics of user-identified fish and non-fish acoustic tracks being utilized as learning samples for the DFA. Also, a method to rank the discriminating power of individual variables is presented, providing guidance for constructing efficient and effective discriminant functions with variables that offer high discriminating power. The DFA yielded classification accuracies of 96% for fish and 91% for non-fish tracks and reduced the manual sorting time by 50–75%.

Wave Forces on Large Objects in the Sea

1971 ◽  
Vol 15 (02) ◽  
pp. 115-124
Author(s):  
Jerome H. Milgram ◽  
John E. Halkyard
Keyword(s):  
Integral Equation ◽  
Vertical Surface ◽  
Numerical Results ◽  
Successive Approximations ◽  
Wave Forces ◽  
Algebraic Equations ◽  
Stationary Objects ◽  
Points Of View ◽  
Axisymmetric Bodies ◽  
Direct Calculations

Three methods of finding wave forces on large, rigid axisymmetric stationary objects in the sea are examined. The problem is formulated in terms of an integral equation and the three methods are successive approximations, direct calculations by means of simultaneous algebraic equations, and use of Haskind's relations. The latter method is included as it offers a large saving of computational efforts for axisymmetric bodies. The applicability of the methods is studied from both the theoretical and applied points of view. Numerical results are given for submerged and floating spheres and for shapes comprising a vertical surface-piercing cylinder atop a sphere.

Still Life Theory

2003 ◽  
Author(s):  
Matthew Cook
Keyword(s):  
Live Cells ◽  
Still Life ◽  
Aircraft Carrier ◽  
Stable Pattern ◽  
Random Grid ◽  
Stationary Objects ◽  
Simple Notion ◽  
Long Term Behavior ◽  
Conway’S Game Of Life

In Conway’s Game of Life [2], if one starts with a large array of randomly set cells, then after around twenty thousand generations one will see that all motion has died down, and only stationary objects of low period remain, providing a final density of about .0287. No methods are known for proving rigorously that this behavior should occur, but it is reliably observed in simulations. This brings up several interesting related questions. Why does this “freezing” occur? After everything has frozen, what is the remaining debris composed of? Is there some construction that can “eat through” the debris? If we start with an infinitely large random grid, so that all constructions appear somewhere, what will the long term behavior be? It seems clear that knowing the composition of typical debris is central to many such questions. Much effort has gone into analyzing the objects that occur in such stationary debris, as well as into determining what stationary objects can exist at all in Life [4, 8], Both of these endeavors depend on having some notion of what an “object” is in the first place. One simple notion is that of an island, a maximal set of live cells connected to each other by paths of purely live cells. But many common objects, such as the “aircraft carrier,” are not connected so strongly. They are composed of more than one island, but we think of them as a single object anyway, since their constituent islands are not separately stable. Any pattern that is stable (has period one, i.e., does not change over time) is called a still life. Since a collection of stable objects can satisfy this definition, the term strict still life is used to refer to a single indivisible stable object, and pseudo still life is used to refer to a stable pattern that is composed of distinct strict still lifes. For example, the bi-block is a pseudo still life, since it is composed of two blocks, but the aircraft carrier is a strict still life, since its islands are not stable on their own.

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