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Bounding overwatch

About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.


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Journal ArticleDOI
TL;DR: An improved stacked Yolov3 model is designed for the detection of objects by bounding boxes and the performance is better than other existing object detection models.
Abstract: Object detection is a stimulating task in the applications of computer vision It is gaining a lot of attention in many real-time applications such as detection of number plates of suspect cars, identifying trespassers under surveillance areas, detecting unmasked faces in security gates during the COVID-19 period, etc Region-based Convolution Neural Networks(R-CNN), You only Look once (YOLO) based CNNs, etc , comes under Deep Learning approaches In this proposed work, an improved stacked Yolov3 model is designed for the detection of objects by bounding boxes Hyperparameters are tuned to get optimum performance The proposed model evaluated using the COCO dataset, and the performance is better than other existing object detection models Anchor boxes are used for overlapping objects After removing all the predicted bounding boxes that have a low detection probability, bounding boxes with the highest detection probability are selected and eliminated all the bounding boxes whose Intersection Over Union value is higher than 0 4 Non-Maximal Suppression (NMS) is used to only keep the best bounding box In this experimentation, we have tried with various range of values, but finally got better result at threshold 0 5 © 2020 International Information and Engineering Technology Association All rights reserved

5 citations

Proceedings ArticleDOI
12 Dec 2005
TL;DR: New methods for adaptively bounding approximation accuracy with methods that involve localized forgetting are developed, which have utility for self-organizing approximators that could adjust the number of basis elements N by adding additional approximation resources in the regions where the approximation error bound is large.
Abstract: This article develops new methods for adaptively bounding approximation accuracy with methods that involve localized forgetting. The existing results use global forgetting. The importance of local versus global forgetting is motivated in the text. Such bounds have utility for self-organizing approximators that could adjust the number of basis elements N by adding additional approximation resources in the regions where the approximation error bound is large.

5 citations

Proceedings Article
22 May 2006
TL;DR: This paper compute a multi-objective lower bound set that, if large enough, can be used to detect the inconsistency of the problem and shows that propagation of additive bounding constraints using this approach is clearly superior than previous approaches.
Abstract: Bounding constraints are used to bound the tolerance of solutions under certain undesirable features. Standard solvers propagate them one by one. Often times, it is easy to satisfy them independently, but difficult to satisfy them simultaneously. Therefore, the standard propagation methods fail. In this paper we propose a novel approach inspired in multi-objective optimization. We compute a multi-objective lower bound set that, if large enough, can be used to detect the inconsistency of the problem. Our experiments on two domains inspired in real-world problems show that propagation of additive bounding constraints using our approach is clearly superior than previous approaches.

5 citations

Journal ArticleDOI
19 Jun 2015
TL;DR: The most popular bounding volumes are spheres, OBBs, Axis-Aligned Bounding Boxes (AABBs), and ellipsoids, cylinders, sphere packing, sphere shells, k-DOPs, convex hulls, cloud of points, and minimal bounding boxes, among others as mentioned in this paper.
Abstract: A bounding volume is a common method to simplify object representation by using the composition of geometrical shapes that enclose the object; it encapsulates complex objects by means of simple volumes and it is widely useful in collision detection applications and ray tracing for rendering algorithms. They are popular in computer graphics and computational geometry. Most popular bounding volumes are spheres, Oriented-Bounding Boxes (OBB’s), Axis-Aligned Bounding Boxes (AABB’s); moreover, the literature review includes ellipsoids, cylinders, sphere packing, sphere shells,k-DOP’s, convex hulls, cloud of points, and minimal bounding boxes, among others. A Bounding Volume Hierarchy is usually a tree in which the complete object is represented tighter fitting every level of the hierarchy. Additionally, each bounding volume as a cost associated to construction, update, and interference tests. For instance, spheres are invariant to rotation and translations, then they do not require being updated; their constructions and interference tests are more straightforward then OBB’s; however, their tightness is lower than other bounding volumes. Finally, three comparisons between two polyhedra; seven different algorithms were used, of which five are public libraries for collision detection.

5 citations

Proceedings Article
01 Jan 2018
TL;DR: A best-first search algorithm is proposed that provides anytime upper bounds for marginal MAP in graphical models and folds the computation of external maximization and internal summation into an AND/OR tree search framework, and solves them simultaneously using a unified best- first search algorithm.
Abstract: Marginal MAP is a key task in Bayesian inference and decision-making. It is known to be very difficult in general, particularly because the evaluation of each MAP assignment requires solving an internal summation problem. In this paper, we propose a best-first search algorithm that provides anytime upper bounds for marginal MAP in graphical models. It folds the computation of external maximization and internal summation into an AND/OR tree search framework, and solves them simultaneously using a unified best-first search algorithm. The algorithm avoids some unnecessary computation of summation sub-problems associated with MAP assignments, and thus yields significant time savings. Furthermore, our algorithm is able to operate within limited memory. Empirical evaluation on three challenging benchmarks demonstrates that our unified best-first search algorithm using pre-compiled variational heuristics often provides tighter anytime upper bounds compared to those state-of-the-art baselines.

5 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023714
20221,629
2021155
202075
201973
201850