Bounding overwatch

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

EFFNet: Enhanced Feature Foreground Network for Video Smoke Source Prediction and Detection

Abstract: Smoke detection in video is a challenging task because of the irregular shape of smoke, its complex motion state, which is affected by temperature, wind and other external factors, and background disturbances. Pixel-based foreground modeling method is a crucial step in many smoke detection systems and can be applied to efficiently focus on a certain object or a specific region to detect movements or anomalies. In video analysis, it is a natural idea to move the focus from the pixel-level foreground to the feature-level foreground. In this paper, the feature foreground is generated by the middle layer of a convolutional neural network (CNN) to guide the temporal modeling process for smoke objects. A novel temporal module called the Feature Foreground Module (FFM) is proposed to boost learning of a smoke temporal representation. Consider the problem of smoke analysis in video, we present a novel unifying approach, named an enhanced feature foreground network (EFFNet), that performs both smoke source prediction and detection. Efficient branch networks are designed in EFFNet, to predict the source mask and bounding boxes of smoke plumes in video. To the best of our knowledge, this is the first paper to study the source of smoke using deep learning methods. Finally, experiments on a realistic smoke dataset and a public dataset show that EFFNet method performs much better than do previous state-of-the-art methods.

TasselNetV3: Explainable Plant Counting With Guided Upsampling and Background Suppression

Abstract: Fast and accurate plant counting tools affect revolution in modern agriculture. Agricultural practitioners, however, expect the output of the tools to be not only accurate but also explainable. Such explainability often refers to the ability to infer which instance is counted. One intuitive way is to generate a bounding box for each instance. Nevertheless, compared with counting by detection, plant counts can be inferred more directly in the local count framework, while one thing reproaching this paradigm is its poor explainability of output visualization. In particular, we find that the poor explainability becomes a bottleneck limiting the counting performance. To address this, we explore the idea of guided upsampling and background suppression where a novel upsampling operator is proposed to allow count redistribution, and segmentation decoders with different fusion strategies are investigated to suppress background, respectively. By integrating them into our previous counting model TasselNetV2, we introduce TasselNetV3 series: TasselNetV3-Lite and TasselNetV3-Seg. We validate the TasselNetV3 series on three public plant counting data sets and a new unmanned aircraft vehicle (UAV)-based data set, covering maize tassels counting, wheat ears counting, and rice plants counting. Extensive results show that guided upsampling and background suppression not only improve counting performance but also enable explainable visualization. Aside from state-of-the-art performance, we have several interesting observations: 1) a limited-receptive-field counter in most cases outperforms a large-receptive-field one; 2) it is sufficient to generate empirical segmentation masks from dotted annotations; 3) middle fusion is a good choice to integrate foreground–background a priori knowledge; and 4) decoupling the learning of counting and segmentation matters.

Oriented Object Detection by Searching Corner Points in Remote Sensing Imagery

Abstract: Oriented object detection in remote sensing images has drawn great attention since it can provide more accurate bounding boxes We propose a one-stage anchor-free network based on searching four corner points of an object, which can yield an arbitrary quadrilateral to fit objects with different shapes and orientations We detect the corners by combining two strategies, where one regresses to the relative corner positions with respect to their corresponding center and the other directly detects the absolute corner positions from the corner heatmaps By defining a candidate corner region based on the regressed results, we check whether corner points from the corner heatmaps are included in the region If so, the closest one relative to the regressed corner is selected as the final position; otherwise, the regressed corner position is utilized Experiments were conducted on two aerial remote sensing datasets, and the results demonstrated that the proposed method achieves superior performance to both the anchor-based and anchor-free methods

Unobservable Selection and Coefficient Stability: Theory and Validation

Abstract: A common heuristic for evaluating robustness of results to omitted variable bias is to look at coefficient movements after inclusion of controls This heuristic is informative only if selection on observables is proportional to selection on unobservables I formalize this link, drawing on theory in Altonji, Elder and Taber (2005) and show how, with this assumption, coefficient movements, along with movements in R-squared values, can be used to calculate omitted variable bias I discuss empirical implementation and describe a formal bounding argument to replace the coefficient movement heuristic I show two validation exercises suggesting that this bounding argument would perform well empirically I discuss application of this procedure to a large set of publications in economics, and use evidence from randomized studies to draw guidelines as to appropriate bounding values