1,721,436 research outputs found
Replication Data for: Slant, Extremity, and Diversity - How the Shape of News Use Explains Electoral Judgments and Confidence
No full textThis dataset contains the replication data for "Slant, Extremity, and Diversity: How the Shape of News Use Explains Electoral Judgments and Confidence
Research on graphene/silicon Schottky junction based photodetector
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Replication Data for: Fostering Accurate Reasoning about Outgroups: Experimental Evidence from Intergroup Relation Priming on Conspiracy Beliefs Amid Sino-U.S. Tensions
No full textThis is the replication files for: Fostering Accurate Reasoning about Outgroups: Experimental Evidence from Intergroup Relation Priming on Conspiracy Beliefs Amid Sino-U.S. Tension
Prioritized target tracking with active collaborative cameras
No full textMobile cameras on robotic platforms can support fixed multi-camera installations to improve coverage and target localization accuracy. We propose a novel collaborative framework for prioritized target tracking that complement static cameras with mobile cameras, which track targets on demand. Upon receiving a request from static cameras, a mobile camera selects (or switches to) a target to track using a local selection criterion that accounts for target priority, view quality and energy consumption. Mobile cameras use a receding horizon scheme to minimize tracking uncertainty as well as energy consumption when planning their path. We validate the proposed framework in simulated realistic scenarios and show that it improves tracking accuracy and target observation time with reduced energy consumption compared to a framework with only static cameras and compared to a state-of-the-art motion strategy
Concurrent Target Following with Active Directional Sensors
No full textWe propose a collision-avoidance tracker for agents with a directional sensor that aim to maintain a moving target in their field of view. The proposed tracker addresses the view maintenance issue within an Optimal Reciprocal Collision Avoidance (ORCA) framework. Our tracking agents adaptively share the responsibility of avoiding each other and minimise with a smooth actuation the deviation angle from their heading direction to their target. Experimental results with real people trajectories from public datasets show that the proposed method improves view maintenance
Coalition formation for distributed tracking in wireless camera networks
No full textWe present a fully distributed framework for multi-target tracking with bandwidth-limited (wireless) camera networks. Cameras self-organize into coalitions to perform the task of distributed target tracking via local interactions. Each camera joins the coalitions based on considerations of marginal utility, which takes into account tracking confidence and communication performance in the neighborhood of the camera. The proposed framework achieves higher tracking accuracy and quicker convergence than decentralized tracking or distributed tracking without coalition formation. Moreover, the communication cost of the proposed framework is considerably reduced compared to distributed tracking without coalition formation and comparable to decentralized tracking as the number of targets increases
Active visual tracking in multi-agent scenarios
No full textWe propose an active visual tracker with collision avoidance for camera-equipped robots in dense multi-agent scenarios. The objective of each tracking agent (robot) is to maintain visual fixation on its moving target while updating its velocity to avoid other agents. However, when multiple robots are present or targets intensively intersect each other, robots may have no accessible collision-avoiding paths. We address this problem with an adaptive mechanism that sets the pair-wise responsibilities to increase the total accessible collision-avoiding controls. The final collision-avoiding control accounts for motion smoothness and view performance, i.e. maintaining the target centered in the field of view and at a certain size. We validate the proposed approach under different target-intersecting scenarios and compare it with the Optimal Reciprocal Collision Avoidance and the Reciprocal Velocity Obstacle methods
Iterative learning control of minimum energy path following tasks for second-order MIMO systems: an indirect reference update framework
No full textIn a large range of manufacturing tasks, the design objective is characterised as following a given path defined in space. In these applications, the tracking time of any particular position along the path is not specified, so an appropriate motion profile can be chosen among its admissible solutions to improve its tracking performance. This paper develops an indirect reference update framework that maximizes accuracy while embedding practical constraints. An optimal path planning problem, incorporating system constraints, is formulated and can be solved using a discretized approach to derive a motion profile that minimizes control energy for a broad spectrum of industrial tasks. To satisfy robustness concerns, an iterative learning control (ILC) algorithm with an indirect reference update framework is designed to improve the accuracy and robustness of path following. It is evaluated on a gantry robot test platform, and the results illustrate superior levels of practical performance in terms of energy reduction and path following accuracy compared with existing approaches
Fast re-OBJ: real-time object re-identification in rigid scenes
No full textRe-identifying objects in a rigid scene across varying viewpoints (object Re-ID) is a challenging task, in particular when there are similar, even identical objects coexist in the same environment. Discriminative features play no doubt an essential role in addressing this challenge, while for practical deployment, real-time performance is another desired attribute. We therefore propose a novel framework, named Fast re-OBJ, that is able to improve both Re-ID accuracy and processing speed via tight coupling between the instance segmentation module and embedding generation module. The rich object encoding in the instance segmentation backbone is directly shared to the embedding generation module for training a more discriminative representation via a triplet network. Moreover, we create datasets with the segmentation outputs using real-time object detectors to train and evaluate our object embedding module. With extensive experiments, we prove that our proposed Fast re-OBJ improves the object Re-ID accuracy by 5% and the speed is 5× faster compared to the state-of-the-art methods. The dataset and code repository are publicly available at: https://tinyurl.com/bdsb53c4
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