Bounding overwatch

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

Bounding expectations of functions of random vectors with given marginals and some moments: Applications of the multivariate discrete moment problem

Abstract: The paper shows how the bounding technique provided by the multivariate discrete moment problem can be used for bounding expectations of functions of random variables with known univariate marginals and some of the mixed moments. Four examples are presented. In the first one the function is a Monge or related type array, in the second one it is a pseudoBoolean function. In the further examples bounds are given for values of multivariate generating functions and expectations of special utility functions of random vectors. Numerical results are presented. Mathematics subject classification (2010): 90C05, 90C15, 62H99, 60E05.

Detection of 3D bounding boxes of vehicles using perspective transformation for accurate speed measurement

Abstract: Detection and tracking of vehicles captured by traffic surveillance cameras is a key component of intelligent transportation systems. We present an improved version of our algorithm for detection of 3D bounding boxes of vehicles, their tracking and subsequent speed estimation. Our algorithm utilizes the known geometry of vanishing points in the surveilled scene to construct a perspective transformation. The transformation enables an intuitive simplification of the problem of detecting 3D bounding boxes to detection of 2D bounding boxes with one additional parameter using a standard 2D object detector. Main contribution of this paper is an improved construction of the perspective transformation which is more robust and fully automatic and an extended experimental evaluation of speed estimation. We test our algorithm on the speed estimation task of the BrnoCompSpeed dataset. We evaluate our approach with different configurations to gauge the relationship between accuracy and computational costs and benefits of 3D bounding box detection over 2D detection. All of the tested configurations run in real time and are fully automatic. Compared to other published state-of-the-art fully automatic results, our algorithm reduces the mean absolute speed measurement error by 32% (1.10 km/h to 0.75 km/h) and the absolute median error by 40% (0.97 km/h to 0.58 km/h).

Bounding Zolotarev numbers using Faber rational functions.

Abstract: By closely following a construction by Ganelius, we construct Faber rational functions that allow us to derive tight and explicit bounds on Zolotarev numbers. We use our results to bound the singular values of matrices, including complex-valued Cauchy matrices and Vandermonde matrices with nodes inside the unit disk. We construct Faber rational functions using doubly-connected conformal maps and use their zeros and poles to supply shift parameters in the alternating direction implicit method.

Efficient Encoder-Decoder Network With Estimated Direction for SAR Ship Detection

Abstract: Synthetic aperture radar (SAR) image ship detection has important applications in marine surveillance. There are two limitations when applying advanced detection methods naively for SAR ship detection. First, most detectors construct the model as an encoder and rely on the feature pyramid network (FPN) head for accurate prediction, which may lead to high computational costs. Second, the background noises in the ground truth (annotated as rectangular bounding boxes) of angular ships bring difficulties for model training. To meet these challenges, we propose an efficient encoder–decoder network with estimated direction for ship detection in SAR images. First, we present an anchor-free encoder–decoder model that can efficiently extract multiple-level features. Second, we formulate ship detection as a multitask learning problem, including a bounding box prediction and a ship direction regression. The estimated ship direction can weakly supervise and benefit ship detection. Furthermore, we develop a center-weighted labeling method for overlapped annotations. Comprehensive experiments on SAR-Ship-Detection and SSDD datasets show that our method achieves state-of-the-art performance with a high running speed.