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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
01 Apr 2022
TL;DR: In this article , the authors use 2-D bounding boxes and object sizes as the only labels and constrain the problem with multiple images of known relative poses during training, which leads to better learning of 6-D pose embeddings in comparison to fully supervised methods.
Abstract: Precise annotation of 6-D poses in real data is intricate and time-consuming, however, an essential requirement to train pose estimation pipelines. We propose a way for scalable, end-to-end 6-D pose regression with weak supervision to avoid this problem. Our method requires neither 3-D models nor 6-D object poses as ground truth. Instead, we use 2-D bounding boxes and object sizes as the only labels and constrain the problem with multiple images of known relative poses during training. A novel Rotated-IoU loss brings together a pose prediction from an image with labeled 2-D bounding boxes of the corresponding object in other views. Our rotation estimation combines an initial coarse pose classification with an offset regression using a continuous rotation parametrization that allows for direct pose estimation. At test time, the model still uses only a single image to predict a 6-D pose. We observe that multi-view constraints and our rotation representation used during training lead to better learning of 6-D pose embeddings in comparison to fully supervised methods. Experiments on several datasets show that the proposed method is capable of predicting poses of good quality, in spite being trained with only weak labels. Direct pose regression without the need for a consecutive refinement stage thereby ensures real-time performance.

5 citations

Journal ArticleDOI
TL;DR: In this paper , a novel finite-time command filtered backstepping approach is presented by using the command-filtering technique, finite time theory, and barrier Lyapunov functions, which can not only reduce the complexity of computation of the conventional backstpping control and compensate filtered errors caused by dynamic surface control but also ensure that the output variables are restricted in compact bounding sets.
Abstract: This work addresses a finite-time tracking control issue for a class of nonlinear systems with asymmetric time-varying output constraints and input nonlinearities. To guarantee the finite-time convergence of tracking errors, a novel finite-time command filtered backstepping approach is presented by using the command filtered backstepping technique, finite-time theory, and barrier Lyapunov functions. The newly proposed method can not only reduce the complexity of computation of the conventional backstepping control and compensate filtered errors caused by dynamic surface control but also can ensure that the output variables are restricted in compact bounding sets. Moreover, the proposed controller is applied to robot manipulator systems, which guarantees the practical boundedness of all the signals in the closed-loop system. Finally, the effectiveness and practicability of the developed control strategy are validated by a simulation example.

5 citations

Patent
24 Sep 1998
TL;DR: In this article, a computer system identifies a predicate in a computer language containing constant expressions as vacuous and represents the predicate by a set of bounding rectangles in a space having a number of dimensions equal to the number of variables.
Abstract: A computer system identifies a predicate in a computer language containing constant expressions as vacuous. The system identifies distinct variables contained in the predicate and represents the predicate by a set of bounding rectangles. The bounding rectangles are represented in a space having a number of dimensions equal to the number of variables. There are finite limits on dimensions of a bounding rectangle which represent the relationship between the variables in the predicate and the constant expressions in the predicate. The predicate is identified as vacuously FALSE where the set of bounding rectangles is empty.

5 citations

Journal ArticleDOI
Yao Xue, Si Liu, Yonghui Li, Ping Wang, Xueming Qian 
TL;DR: Wang et al. as discussed by the authors proposed a pseudo-supervised surgical tool detection (PSTD) framework, which performs explicit detection refinement by three levels of associated measures (pseudo bounding box generation, real box regression, weighted boxes fusion) in a weakly supervised manner.
Abstract: Surgical tool detection is a recently active research area. It is the foundation to a series of advanced surgical support functions, such as image guided surgical navigation, forming safety zone between surgical tools and sensitive tissues. Previous methods rely on two types of information: tool locating signals and vision features. Collecting tool locating signals requires additional hardware equipments. Vision based methods train their detection models using strong annotations (e.g. bounding boxes), which are quite rare and expensive to acquire in the field of surgical image understanding. In this paper, we propose a Pseudo Supervised surgical Tool detection (PSTD) framework, which performs explicit detection refinement by three levels of associated measures (pseudo bounding box generation, real box regression, weighted boxes fusion) in a weakly supervised manner. On the basis of PSTD, we develop a Bi-directional Adaption Weighting (BAW) mechanism in our tool classifier for contextual information mining by creating competition or cooperation relationships between channels. By only using image-level tool category labels, the proposed method yields state-of-the-art results with 87.0% mAP on a mainstream surgical image dataset: Cheloc80.

5 citations

Proceedings ArticleDOI
01 Jan 2022
TL;DR: Gabriellav2 as mentioned in this paper proposed a real-time online activity detection system which can generalize robustly on any unknown facility surveillance videos, which mainly consists of tracklet generation, tracklet activity classification, and prediction refinement using the proposed post-processing algorithm.
Abstract: Activity detection has wide-reaching applications in video surveillance, sports, and behavior analysis. The existing literature in activity detection has mainly focused on benchmarks like AVA, AVA-Kinetics, UCF101-24, and JHMDB-21. However, these datasets fail to address all issues of real-world surveillance camera videos like untrimmed nature, tiny actor bounding boxes, multi-label nature of the actions, etc. In this work, we propose a real-time, online, action detection system which can generalize robustly on any unknown facility surveillance videos. Our real-time system mainly consists of tracklet generation, tracklet activity classification, and prediction refinement using the proposed post-processing algorithm. We tackle the challenging nature of action classification problem in various aspects like handling the class-imbalance training using PLM method and learning multi-label action correlations using LSEP loss. In order to improve the computational efficiency of the system, we utilize knowledge distillation. Our approach gets state-of-the-art performance on ActEV-SDL UF-full dataset and second place in TRECVID 2021 ActEV challenge. Project Webpage: www.crcv.ucf.edu/research/projects/gabriellav2/

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