Bounding overwatch

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

A Formal Framework for Cryptanalyzing RFID Distance Bounding Protocols

Abstract: Many distance bounding protocols appropriate for RFID technology have been proposed recently. However, the design and the analysis of these protocols are not based on a formal perspective. Motivated by this need, a formal framework is presented that helps the future attempts to cryptanalyze and design new distance bounding protocols. We first formalize the adversary scenarios, the protocol means, and the adversary goals in general. Then, we focus on the formalism for RFID systems by describing and extending the adversary strategies and the prover model. Two recently published distance bounding protocols are cryptanalyzed using our formal framework to demonstrate its relevancy and efficiency. Our formalism thus allows to prove that the adversary success probabilities are higher than the originally claimed ones.

Bounding methods for facilities location algorithms

Abstract: Single- and multi-facility location problems are often solved with iterative computational procedures Although these procedures have proven to converage, in practice it is desirable to be able to compute a lower bound on the objective function at each iteration This enables the user to stop the iterative process when the objective function is within a prespecified tolerance of the optimum value In this article we generalize a new bounding method to include multi-facility problems with lp distances A proof is given that for Euclidean distance problems the new bounding procedure is superior to two other known methods Numerical results are given for the three methods

A new branch-and-bound algorithm for standard quadratic programming problems

Abstract: In this paper we propose convex and LP bounds for standard quadratic programming (StQP) problems and employ them within a branch-and-bound approach. We first compare different bounding strategies for StQPs in terms both of the quality of the bound and of the computation times. It turns out that the polyhedral bounding strategy is the best one to be used within a branch-and-bound scheme. Indeed, it guarantees a good quality of the bound at the expense of a very limited computation time. The proposed branch-and-bound algorithm performs an implicit enumeration of all the KKT (stationary) points of the problem. We compare different branching strategies exploiting the structure of the problem. Numerical results on randomly generated problems (with varying density of the underlying convexity graph) are reported which show the effectiveness of the proposed approach, in particular in limiting the growth of the number of nodes in the branch-and-bound tree as the density of the underlying graph increases.

One More Check: Making “Fake Background” Be Tracked Again

Abstract: The one-shot multi-object tracking, which integrates object detection and ID embedding extraction into a unified network, has achieved groundbreaking results in recent years. However, current one-shot trackers solely rely on single-frame detections to predict candidate bounding boxes, which may be unreliable when facing disastrous visual degradation, e.g., motion blur, occlusions. Once a target bounding box is mistakenly classified as background by the detector, the temporal consistency of its corresponding tracklet will be no longer maintained. In this paper, we set out to restore the bounding boxes misclassified as ``fake background'' by proposing a re-check network. The re-check network innovatively expands the role of ID embedding from data association to motion forecasting by effectively propagating previous tracklets to the current frame with a small overhead. Note that the propagation results are yielded by an independent and efficient embedding search, preventing the model from over-relying on detection results. Eventually, it helps to reload the ``fake background'' and repair the broken tracklets. Building on a strong baseline CSTrack, we construct a new one-shot tracker and achieve favorable gains by 70.7 ➡ 76.4, 70.6 ➡ 76.3 MOTA on MOT16 and MOT17, respectively. It also reaches a new state-of-the-art MOTA and IDF1 performance. Code is released at https://github.com/JudasDie/SOTS.

Oriented Bounding Boxes for Small and Freely Rotated Objects

Abstract: A novel object detection method is presented that handles freely rotated objects of arbitrary sizes, including tiny objects as small as $2\times 2$ pixels. Such tiny objects appear frequently in remotely sensed images, and present a challenge to recent object detection algorithms. More importantly, current object detection methods have been designed originally to accommodate axis-aligned bounding box detection, and therefore fail to accurately localize oriented boxes that best describe freely rotated objects. In contrast, the proposed CNN-based approach uses potential pixel information at multiple scale levels without the need for any external resources, such as anchor boxes.The method encodes the precise location and orientation of features of the target objects at grid cell locations. Unlike existing methods which regress the bounding box location and dimension,the proposed method learns all the required information by classification, which has the added benefit of enabling oriented bounding box detection without any extra computation. It thus infers the bounding boxes only at inference time by finding the minimum surrounding box for every set of the same predicted class labels. Moreover, a rotation-invariant feature representation is applied to each scale, which imposes a regularization constraint to enforce covering the 360 degree range of in-plane rotation of the training samples to share similar features. Evaluations on the xView and DOTA datasets show that the proposed method uniformly improves performance over existing state-of-the-art methods.