In this paper we propose an energy-efficient object tracking algorithm in wireless sensor networks (WSNs). Such sensor networks have to be designed to achieve energy-efficient object tracking for any given arbitrary topology. We consider in particular the bi-directional moving objects with given frequencies for each pair of sensor nodes and link transmission cost. This problem is formulated as a 0/1 integer-programming problem. A Lagrangean relaxation-based (LR-based) heuristic algorithm is proposed for solving the optimization problem. Experimental results showed that the proposed algorithm achieves near optimization in energy-efficient object tracking. Furthermore, the algorithm is very efficient and scalable in terms of the solution time.
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http://dx.doi.org/10.3390/s100908101 | DOI Listing |
Infancy
January 2025
Institute of Child Development, University of Minnesota, Minneapolis, Minnesota, USA.
East Asians are more likely than North Americans to attend to visual scenes holistically, focusing on the relations between objects and their background rather than isolating components. This cultural difference in context sensitivity-greater attentional allocation to the background of an image or scene-has been attributed to socialization, yet it is unknown how early in development it appears, and whether it is moderated by social information. We employed eye-tracking to investigate context-sensitivity in 15-month-olds in Japan (n = 45) and the United States (n = 52).
View Article and Find Full Text PDFSci Rep
January 2025
School of Electrical and Control Engineering, North China University of Technology, Beijing, China.
This paper proposes a new strategy for analysing and detecting abnormal passenger behavior and abnormal objects on buses. First, a library of abnormal passenger behaviors and objects on buses is established. Then, a new mask detection and abnormal object detection and analysis (MD-AODA) algorithm is proposed.
View Article and Find Full Text PDFHealthc Technol Lett
December 2024
Robotics and Control Laboratory, Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada.
The Segment Anything model (SAM) is a powerful vision foundation model that is revolutionizing the traditional paradigm of segmentation. Despite this, a reliance on prompting each frame and large computational cost limit its usage in robotically assisted surgery. Applications, such as augmented reality guidance, require little user intervention along with efficient inference to be usable clinically.
View Article and Find Full Text PDFData Brief
February 2025
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, 48128 MI, USA.
In this data article, we introduce the Multi-Modal Event-based Vehicle Detection and Tracking (MEVDT) dataset. This dataset provides a synchronized stream of event data and grayscale images of traffic scenes, captured using the Dynamic and Active-Pixel Vision Sensor (DAVIS) 240c hybrid event-based camera. MEVDT comprises 63 multi-modal sequences with approximately 13k images, 5M events, 10k object labels, and 85 unique object tracking trajectories.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Accurate detection and tracking of dynamic objects are critical for enabling skill demonstration and effective skill generalization in robotic skill learning and application scenarios. To further improve the detection accuracy and tracking speed of the YOLOv8s model in dynamic object tracking tasks, this paper proposes a method to enhance both detection precision and speed based on YOLOv8s architecture. Specifically, a Focused Linear Attention mechanism is introduced into the YOLOv8s backbone network to enhance dynamic object detection accuracy, while the Ghost module is incorporated into the neck network to improve the model's tracking speed for dynamic objects.
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