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Journal articles on the topic 'Single object'

Author: Grafiati

Published: 10 March 2023

Last updated: 25 July 2025

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1

Dong, Yu Bing, Ying Sun, and Ming Jing Li. "Multi-Object Tracking with Single Camera." Applied Mechanics and Materials 740 (March 2015): 668–71. http://dx.doi.org/10.4028/www.scientific.net/amm.740.668.

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Multi-object tracking has been a challenging topic in computer vision. A Simple and efficient moving multi-object tracking algorithm is proposed. A new tracking method combined with trajectory prediction and a sub-block matching is used to handle the objects occlusion. The experimental results show that the proposed algorithm has good performance.

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Moscatelli, Alberto. "A single object rotating." Nature Nanotechnology 13, no. 9 (2018): 769. http://dx.doi.org/10.1038/s41565-018-0265-1.

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Ren, Xiaoyuan, Libing Jiang, and Zhuang Wang. "Pose Estimation of Uncooperative Unknown Space Objects from a Single Image." International Journal of Aerospace Engineering 2020 (July 18, 2020): 1–9. http://dx.doi.org/10.1155/2020/9966311.

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Estimating the 3D pose of the space object from a single image is an important but challenging work. Most of the existing methods estimate the 3D pose of known space objects and assume that the detailed geometry of a specific object is known. These methods are not available for unknown objects without the known geometry of the object. In contrast to previous works, this paper devotes to estimate the 3D pose of the unknown space object from a single image. Our method estimates not only the pose but also the shape of the unknown object from a single image. In this paper, a hierarchical shape mod

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Lee, Wooju, Dasol Hong, Hyungtae Lim, and Hyun Myung. "Object-Aware Domain Generalization for Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 2947–55. http://dx.doi.org/10.1609/aaai.v38i4.28076.

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Single-domain generalization (S-DG) aims to generalize a model to unseen environments with a single-source domain. However, most S-DG approaches have been conducted in the field of classification. When these approaches are applied to object detection, the semantic features of some objects can be damaged, which can lead to imprecise object localization and misclassification. To address these problems, we propose an object-aware domain generalization (OA-DG) method for single-domain generalization in object detection. Our method consists of data augmentation and training strategy, which are call

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Champion, Benjamin, Mo Jamshidi, and Matthew Joordens. "Depth Estimation of an Underwater Object Using a Single Camera." KnE Engineering 2, no. 2 (2017): 112. http://dx.doi.org/10.18502/keg.v2i2.603.

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<p>Underwater robotics is currently a growing field. To be able to autonomously find and collect objects on the land and in the air is a complicated problem, which is only compounded within the underwater setting. Different techniques have been developed over the years to attempt to solve this problem, many of which involve the use of expensive sensors. This paper explores a method to find the depth of an object within the underwater setting, using a single camera source and a known object. Once this known object has been found, information about other unknown objects surrounding this po

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Osiurak, François, Ghislaine Aubin, Philippe Allain, Christophe Jarry, Isabelle Richard, and Didier Le Gall. "Object utilization and object usage: A single-case study." Neurocase 14, no. 2 (2008): 169–83. http://dx.doi.org/10.1080/13554790802108372.

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Dai, Ziying, Xiaoguang Mao, Yan Lei, Yuhua Qi, Rui Wang, and Bin Gu. "Compositional Mining of Multiple Object API Protocols through State Abstraction." Scientific World Journal 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/171647.

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API protocols specify correct sequences of method invocations. Despite their usefulness, API protocols are often unavailable in practice because writing them is cumbersome and error prone. Multiple object API protocols are more expressive than single object API protocols. However, the huge number of objects of typical object-oriented programs poses a major challenge to the automatic mining of multiple object API protocols: besides maintaining scalability, it is important to capture various object interactions. Current approaches utilize various heuristics to focus on small sets of methods. In

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Fernández, Unai J., Sonia Elizondo, Naroa Iriarte, et al. "A Multi-Object Grasp Technique for Placement of Objects in Virtual Reality." Applied Sciences 12, no. 9 (2022): 4193. http://dx.doi.org/10.3390/app12094193.

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Some daily tasks involve grasping multiple objects in one hand and releasing them in a determined order, for example laying out a surgical table or distributing items on shelves. For training these tasks in Virtual Reality (VR), there is no technique for allowing users to grasp multiple objects in one hand in a realistic way, and it is not known if such a technique would benefit user experience. Here, we design a multi-object grasp technique that enables users to grasp multiple objects in one hand and release them in a controlled way. We tested an object placement task under three conditions:

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Chung-Hyok, O., Jo Se-Ung, Ri Chang-Yong, and Om Chol-Nam. "SINGLE-SHOT DETECTOR USING ADAPTIVE ANCHORS." ICTACT Journal on Image and Video Processing 15, no. 4 (2025): 3613–19. https://doi.org/10.21917/ijivp.2025.0511.

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Slim-object detection is one of the challenging problems in image processing because the shape (or bounding box) of a slim object changes a lot according to the viewpoint. However, so far there has been a lot of investigations on small-object detection problems. In addition, most of investigations were focused on effective feature extractions. However, only with the effective feature extraction slim-object detection problems are not manipulated properly because of the large-scale varying proportions of bounding boxes. In general, most of single-shot detectors use anchors to detect several obje

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Abraham, Glincy, K. A. Narayanankutty, and K. P. Soman. "Sparsity based Single Object Tracking." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 9, no. 2 (2013): 1004–11. http://dx.doi.org/10.24297/ijct.v9i2.4167.

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Object tracking has importance in various video processing applications like video surveillance, perceptual user interface driver assistance, tracking etc. This paper deals with a new tracking technique that combines the dictionary based background subtraction along with sparsity based tracking. The speed and performance challenges faced during the sparsity based tracking alone are addressed, as it is based on a background subtraction preprocessing and local compressive tracking. It also overcomes the challenges faced by the traditional techniques due to illumination variation, pose and shape

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Alonso, Estrella, and Juan Tejada. "Risk optimal single-object auctions." Cuadernos de Economía 35, no. 99 (2012): 131–38. http://dx.doi.org/10.1016/s0210-0266(12)70030-4.

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Mishra, Debasis, and Abdul Quadir. "Non-bossy single object auctions." Economic Theory Bulletin 2, no. 1 (2014): 93–110. http://dx.doi.org/10.1007/s40505-014-0031-y.

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Ji, Pengcheng, Qingfan Wu, Shengfu Cao, Huijuan Zhang, Zhaohua Yang, and Yuanjin Yu. "Single-pixel imaging of a moving object with multi-motion." Chinese Optics Letters 22, no. 10 (2024): 101101. http://dx.doi.org/10.3788/col202422.101101.

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Yow, Kin-Choong, and Insu Kim. "General Moving Object Localization from a Single Flying Camera." Applied Sciences 10, no. 19 (2020): 6945. http://dx.doi.org/10.3390/app10196945.

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Object localization is an important task in the visual surveillance of scenes, and it has important applications in locating personnel and/or equipment in large open spaces such as a farm or a mine. Traditionally, object localization can be performed using the technique of stereo vision: using two fixed cameras for a moving object, or using a single moving camera for a stationary object. This research addresses the problem of determining the location of a moving object using only a single moving camera, and it does not make use of any prior information on the type of object nor the size of the

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Cheong, Yun Zhe, and Wei Jen Chew. "The Application of Image Processing to Solve Occlusion Issue in Object Tracking." MATEC Web of Conferences 152 (2018): 03001. http://dx.doi.org/10.1051/matecconf/201815203001.

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Object tracking is a computer vision field that involves identifying and tracking either a single or multiple objects in an environment. This is extremely useful to help observe the movements of the target object like people in the street or cars on the road. However, a common issue with tracking an object in an environment with many moving objects is occlusion. Occlusion can cause the system to lose track of the object being tracked or after overlapping, the wrong object will be tracked instead. In this paper, a system that is able to correctly track occluded objects is proposed. This system

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Pinilla, Samuel, Laura Galvis, Karen Egiazarian, and Henry Arguello. "Single-shot Coded Diffraction System for 3D Object Shape Estimation." Electronic Imaging 2020, no. 14 (2020): 59–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.14.coimg-059.

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The three-dimensional (3D) shape reconstruction problem of an object is a task of high interest in autonomous vehicles, detection of moving objects, and precision agriculture. A common methodology to recover the 3D shape of an object is using its optical phase. However, this approach involves solving a non-convex computationally demanding inverse problem known as phase retrieval (PR) in a setup that records coded diffraction patterns (CDP). Usually, the acquisition of several snapshots from the scene is required to solve the PR problem. This work proposes a single-shot 3D shape estimation tech

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Maktab Dar Oghaz, Mahdi, Manzoor Razaak, and Paolo Remagnino. "Enhanced Single Shot Small Object Detector for Aerial Imagery Using Super-Resolution, Feature Fusion and Deconvolution." Sensors 22, no. 12 (2022): 4339. http://dx.doi.org/10.3390/s22124339.

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One common issue of object detection in aerial imagery is the small size of objects in proportion to the overall image size. This is mainly caused by high camera altitude and wide-angle lenses that are commonly used in drones aimed to maximize the coverage. State-of-the-art general purpose object detector tend to under-perform and struggle with small object detection due to loss of spatial features and weak feature representation of the small objects and sheer imbalance between objects and the background. This paper aims to address small object detection in aerial imagery by offering a Convolu

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Yu, Yong Yan, and Zhi Jian Wang. "Research on 3D Reconstruction Based on a Single Image." Advanced Materials Research 108-111 (May 2010): 3–10. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.3.

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Aimed at the fact that most of the objects was symmetrical, we present a method reconstructing 3D model from single 2D image. At first, the angle, inclination angle and pivot angle should be established in the ternary perspective transformed matrix T, then the calibration which indicated the outline of the object can pick up the characteristic line. It resolved the host extinguishes information in accordance with the similar features of the parallel group of lines projection angle, then ensured the viewpoint situation and the object plane of symmetry, and made use of the object imagination pla

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Zhao, Dong, Baoqing Ding, Yulin Wu, Lei Chen, and Hongchao Zhou. "Unsupervised Learning from Videos for Object Discovery in Single Images." Symmetry 13, no. 1 (2020): 38. http://dx.doi.org/10.3390/sym13010038.

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This paper proposes a method for discovering the primary objects in single images by learning from videos in a purely unsupervised manner—the learning process is based on videos, but the generated network is able to discover objects from a single input image. The rough idea is that an image typically consists of multiple object instances (like the foreground and background) that have spatial transformations across video frames and they can be sparsely represented. By exploring the sparsity representation of a video with a neural network, one may learn the features of each object instance witho

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Mathai, Anumol, Ningqun Guo, Dong Liu, and Xin Wang. "3D Transparent Object Detection and Reconstruction Based on Passive Mode Single-Pixel Imaging." Sensors 20, no. 15 (2020): 4211. http://dx.doi.org/10.3390/s20154211.

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Transparent object detection and reconstruction are significant, due to their practical applications. The appearance and characteristics of light in these objects make reconstruction methods tailored for Lambertian surfaces fail disgracefully. In this paper, we introduce a fixed multi-viewpoint approach to ascertain the shape of transparent objects, thereby avoiding the rotation or movement of the object during imaging. In addition, a simple and cost-effective experimental setup is presented, which employs two single-pixel detectors and a digital micromirror device, for imaging transparent obj

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Ortega, Ana, and Mubarak Shah. "From Shape from Shading to Object Recognition." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 07 (1998): 969–84. http://dx.doi.org/10.1142/s0218001498000531.

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Recognition of objects is one of the main goals of computer vision. Several approaches have been proposed to solve this problem using 3-D shapes. In most of them it is assumed that the 3-D shape (depth map) is available. Several object recognition systems use range images to extract the 3-D shape. We present a method that uses a shape from shading algorithm to perform 3-D object recognition for simple objects. This method extracts the 3-D information from a single intensity image, then segments the object into regions. After computing the properties of the regions, it compares the input object

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Prathamesh, Sonawane, Gudur Rupa, Gaikwad Vedant, and Jadhav Harshad. "Live Object Recognition using YOLO." Live Object Recognition using YOLO 8, no. 11 (2023): 5. https://doi.org/10.5281/zenodo.10109998.

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- Live object recognition refers to the real-time process of identifying and categorizing objects within a given visual input, such as images. This technology utilizes computer vision techniques and advanced algorithms to detect objects, determine their dimension, area and weight and often classify them into predefined categories. Our system proposes R-CNN and YOLO to determine the dimensions of the objects in real time. YOLO takes a different approach by treating object detection as a single regression problem. A single neural network is trained to directly predict bounding boxes and class pr

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White, Olivier, Noreen Dowling, R. Martyn Bracewell, and Jörn Diedrichsen. "Hand Interactions in Rapid Grip Force Adjustments Are Independent of Object Dynamics." Journal of Neurophysiology 100, no. 5 (2008): 2738–45. http://dx.doi.org/10.1152/jn.90593.2008.

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Object manipulation requires rapid increase in grip force to prevent slippage when the load force of the object suddenly increases. Previous experiments have shown that grip force reactions interact between the hands when holding a single object. Here we test whether this interaction is modulated by the object dynamics experienced before the perturbation of the load force. We hypothesized that coupling of grip forces should be stronger when holding a single object than when holding separate objects. We measured the grip force reactions elicited by unpredictable load perturbations when particip

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Igor, Ruban, Khudov Hennadii, Lishchenko Vitaliy, et al. "Assessing the detection zones of radar stations with the additional use of radiation from external sources." Eastern-European Journal of Enterprise Technologies 6, no. 9 (108) (2020): 6–17. https://doi.org/10.15587/1729-4061.2020.216118.

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This paper reports the assessment of the detection zone of survey radar stations under a mode of single-place location. The detection zone under this mode significantly depends on the properties of the single-position effective surface of air objects scattering. The assessment of the detection zone of survey radar stations under a mode of the distributed location has been performed. It was established that the dimensions of the detection zone of air objects under a mode of the distributed location depend not only on the characteristics of the transmitting and receiving positions but on the sys

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An, Na, and Wei Qi Yan. "Multitarget Tracking Using Siamese Neural Networks." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 2s (2021): 1–16. http://dx.doi.org/10.1145/3441656.

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In this article, we detect and track visual objects by using Siamese network or twin neural network. The Siamese network is constructed to classify moving objects based on the associations of object detection network and object tracking network, which are thought of as the two branches of the twin neural network. The proposed tracking method was designed for single-target tracking, which implements multitarget tracking by using deep neural networks and object detection. The contributions of this article are stated as follows. First, we implement the proposed method for visual object tracking b

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Kim, Changwon. "Robust Single-Image Dehazing." Electronics 10, no. 21 (2021): 2636. http://dx.doi.org/10.3390/electronics10212636.

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This paper proposes a new single-image dehazing method, which is an important preprocessing step in vision applications to overcome the limitations of the conventional dark channel prior. The dark channel prior has a tendency to underestimate transmissions of bright regions or objects that can generate color distortions during the process of dehazing. In order to suppress the distortions in a large sky area or a bright white object, the sky probabilities and the white-object probabilities calculated in the non-sky area are proposed. The sky area is detected by combining the advantages of a reg

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Dawn, Wilson, Manusankar C. Dr., and Prathibha P. H. Dr. "Analytical Study on Object Detection using Yolo Algorithm." International Journal of Innovative Science and Research Technology 7, no. 8 (2022): 587–89. https://doi.org/10.5281/zenodo.7036535.

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Object detection is a technique that allows detecting and locating objects in videos and images. Object detection is widely used to count objects in a scene, track their precise locations and accurately label the objects. It seeks to answer what is the object? and Where is it? . Object detection adopts various approaches such as fast R-CNN, Retina-Net, Single Shot MultiBox Detector (SSD) and YOLO. Among these, YOLO is the most powerful algorithm for object detection and as well as suited for real-time scenarios. It is popular because of its accuracy and speed. YOLO uses Neural networks to prov

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Medvedev, Mikhail, Viacheslav Pshikhopov, and Igor Evdokimov. "A Robust Control Algorithm for Single Input Single Output Dynamic Object Based on Table-Based Q-Method of Reinforcement Learning." Informatics and Automation 24, no. 3 (2025): 717–44. https://doi.org/10.15622/ia.24.3.1.

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The article provides an overview in the field of dynamic object control systems based on reinforcement learning. Based on the analysis, it is concluded that the development of control methods based on reinforcement learning is relevant. The article proposes an intelligent algorithm for robust control of stable dynamic objects with one input and one output, based on the tabular Q-learning method of zero order. The algorithm stabilizes the output value of the control object with a given error if the parameters and external disturbances of the object are piecewise constant unknown quantities, and

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Sonawane, Prathamesh, Rupa Gudur, Vedant Gaikwad, and Harshad Jadhav. "Live Object Recognition Using YOLO." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 2002–6. http://dx.doi.org/10.22214/ijraset.2024.60228.

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Abstract: Live object recognition refers to the real-time process of identifying and categorizing objects within a given visual input, such as images. This technology utilizes computer vision techniques and advanced algorithms to detect objects, determine their dimension, area and weight and often classify them into defined categories. Our system proposesR-CNN and YOLO to determine the dimensions of the objects in real time. YOLO takes a different approach by treating object detection as a single regression problem. A single neural network is trained to directly predict bounding boxes and clas

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Trifunovic, Dejan. "Single object auctions with interdependent values." Ekonomski anali 56, no. 188 (2011): 125–69. http://dx.doi.org/10.2298/eka1188125t.

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This paper reviews single object auctions when bidders? values of the object are interdependent. We will see how the auction forms could be ranked in terms of expected revenue when signals that bidders have about the value of the object are affiliated. In the discussion that follows we will deal with reserve prices and entry fees. Furthermore we will examine the conditions that have to be met for English auction with asymmetric bidders to allocate the object efficiently. Finally, common value auctions will be considered when all bidders have the same value for the object.

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Koning, Arno, and Johan Wagemans. "Detection of Symmetry and Repetition in One and Two Objects." Experimental Psychology 56, no. 1 (2009): 5–17. http://dx.doi.org/10.1027/1618-3169.56.1.5.

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Symmetry is usually easier to detect within a single object than in two objects (one-object advantage), while the reverse is true for repetition (two-objects advantage). This interaction between regularity and number of objects could reflect an intrinsic property of encoding spatial relations within and across objects or it could reflect a matching strategy. To test this, regularities between two contours (belonging to a single object or two objects) had to be detected in two experiments. Projected three-dimensional (3-D) objects rotated in depth were used to disambiguate figure-ground segment

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Chen, Sijia, En Yu, and Wenbing Tao. "Cross-View Referring Multi-Object Tracking." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 2 (2025): 2204–11. https://doi.org/10.1609/aaai.v39i2.32219.

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Referring Multi-Object Tracking (RMOT) is an important topic in the current tracking field. Its task form is to guide the tracker to track objects that match the language description. Current research mainly focuses on referring multi-object tracking under single-view, which refers to a view sequence or multiple unrelated view sequences. However, in the single-view, some appearances of objects are easily invisible, resulting in incorrect matching of objects with the language description. In this work, we propose a new task, called Cross-view Referring Multi-Object Tracking (CRMOT). It introduc

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Wiesmann, Sandro L., and Melissa L. H. Vo. "Is one object enough? Diagnosticity of single objects for fast scene categorization." Journal of Vision 22, no. 14 (2022): 4146. http://dx.doi.org/10.1167/jov.22.14.4146.

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Gorbatsevich, V., Y. Vizilter, V. Knyaz, and A. Moiseenko. "SINGLE-SHOT SEMANTIC MATCHER FOR UNSEEN OBJECT DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 379–84. http://dx.doi.org/10.5194/isprs-archives-xlii-2-379-2018.

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In this paper we combine the ideas of image matching, object detection, image retrieval and zero-shot learning for stating and solving the semantic matching problem. Semantic matcher takes two images (test and request) as input and returns detected objects (bounding boxes) on test image corresponding to semantic class represented by request (sample) image. We implement our single-shot semantic matcher CNN architecture based on GoogleNet and YOLO/DetectNet architectures. We propose the detection-by-request training and testing protocols for semantic matching algorithms. We train and test our CN

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Xie, Rishan, Huixia Jia, Jiawei Chen, Lite Zhang, and Chengwei Zhang. "Research of the Influence of Lateral Inflow Angles on the Cavitation Flow and Movement Characteristics of Underwater Moving Objects." Processes 12, no. 6 (2024): 1051. http://dx.doi.org/10.3390/pr12061051.

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This study examined the multi-phase flow field for a single object and two parallel/series objects under different incoming angles of lateral flow. The volume of fluid model, the Sauer–Schnerr cavitation model, and the six degrees of freedom (DOF) method were adopted to consider simulations of multi-phase flow, phase change, and object movement, respectively. The results show that, for a single object, the degree of asymmetry in the cavity profile depends on the component (the z-component) of the lateral inflow velocity in the direction perpendicular to the initial velocity of the object. As t

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Wang, Jun, Lili Jiang, Qingwen Qi, and Yongji Wang. "Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images." ISPRS International Journal of Geo-Information 10, no. 6 (2021): 420. http://dx.doi.org/10.3390/ijgi10060420.

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Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of dif

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Barbadekar, Ashwinee. "Conversion of an Entire Image into a 3D Scene with Object Detection and Single 3D Models." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 3281–87. http://dx.doi.org/10.22214/ijraset.2024.62319.

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Abstract: The paper presents a framework for the extraction of objects from 2D images using object detection techniques in open CV and then generating 3D models of the segmented objects. To achieve precise object detection, the suggested methodology blends deep learning and computer vision methods. The 3D models that are produced have fine-grained geometric and textural features, which improves the object reconstruction's accuracy. The usefulness and robustness of our technique in various settings are demonstrated by experimental findings on a variety of datasets. For use in robotics, virtual

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Peng, Jiansheng, Kui Fu, Qingjin Wei, Yong Qin, and Qiwen He. "Improved Multiview Decomposition for Single-Image High-Resolution 3D Object Reconstruction." Wireless Communications and Mobile Computing 2020 (December 26, 2020): 1–14. http://dx.doi.org/10.1155/2020/8871082.

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As a representative technology of artificial intelligence, 3D reconstruction based on deep learning can be integrated into the edge computing framework to form an intelligent edge and then realize the intelligent processing of the edge. Recently, high-resolution representation of 3D objects using multiview decomposition (MVD) architecture is a fast reconstruction method for generating objects with realistic details from a single RGB image. The results of high-resolution 3D object reconstruction are related to two aspects. On the one hand, a low-resolution reconstruction network represents a go

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Bouraya, Sara, and Abdessamad Belangour. "Categorizing Video Datasets: Video Object Detection, Multiple and Single Object Tracking." International Journal of Engineering Trends and Technology 72, no. 3 (2024): 99–105. http://dx.doi.org/10.14445/22315381/ijett-v72i3p110.

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Gogel, Walter C., and Thomas J. Sharkey. "Measuring Attention Using Induced Motion." Perception 18, no. 3 (1989): 303–20. http://dx.doi.org/10.1068/p180303.

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Attention was measured by means of its effect upon induced motion. Perceived horizontal motion was induced in a vertically moving test spot by the physical horizontal motion of inducing objects. All stimuli were in a frontoparallel plane. The induced motion vectored with the physical motion to produce a clockwise or counterclockwise tilt in the apparent path of motion of the test spot. Either a single inducing object or two inducing objects moving in opposite directions were used. Twelve observers were instructed to attend to or to ignore the single inducing object while fixating the test obje

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Bahmanyar, R., S. M. Azimi, and P. Reinartz. "MULTIPLE VEHICLES AND PEOPLE TRACKING IN AERIAL IMAGERY USING STACK OF MICRO SINGLE-OBJECT-TRACKING CNNS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 163–70. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-163-2019.

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Abstract. Geo-referenced real-time vehicle and person tracking in aerial imagery has a variety of applications such as traffic and large-scale event monitoring, disaster management, and also for input into predictive traffic and crowd models. However, object tracking in aerial imagery is still an unsolved challenging problem due to the tiny size of the objects as well as different scales and the limited temporal resolution of geo-referenced datasets. In this work, we propose a new approach based on Convolutional Neural Networks (CNNs) to track multiple vehicles and people in aerial image seque

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Dubanov, Aleksandr. "GEOMETRIC MODEL OF GROUP PURSUIT OF A SINGLE TARGET BY THE CHASE METHOD." Geometry & Graphics 10, no. 2 (2022): 20–26. http://dx.doi.org/10.12737/2308-4898-2022-10-2-20-26.

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The article describes the model of group pursuit of a single target by the chase method. All objects participating in the pursuit model move with a constant modulo speed. One of the participants in the process moves along a certain trajectory and releases objects at specified intervals, the task of which is to achieve the goal by the chase method. All objects have restrictions on the curvature of the motion path. A single target, in turn, is tasked with achieving the target that releases objects using the parallel approach method. For each pursuing object, a detection area is formed in the for

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Sharma, Aman Kumar. "Object Detection Model: An Enhanced SSD." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1598–612. http://dx.doi.org/10.22214/ijraset.2024.61802.

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Abstract: Object Detection plays a significant role in today’s era. Object detection plays a crucial role in various applications such as surveillance, medical images, and autonomous vehicles. The primary goal of object detection is to accurately detect the boundaries of objects of interest and classify those objects into predefined categories. In this research paper, we enhanced an object detection technique i.e. Single shot multibox detector (SSD) which is one of the top object detection technique in both aspect accuracy and speed. SSD is an object detection model that predicts object boundi

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Showers, William M., Jairav Desai, Krysta L. Engel, Clayton Smith, Craig T. Jordan, and Austin E. Gillen. "SCUBA implements a storage format-agnostic API for single-cell data access in R." F1000Research 13 (October 21, 2024): 1256. http://dx.doi.org/10.12688/f1000research.154675.1.

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While robust tools exist for the analysis of single-cell datasets in both Python and R, interoperability is limited, and analysis tools generally only accept one object class. Considerable programming expertise is required to integrate tools across package ecosystems into a comprehensive analysis, due to their differing languages and internal data structures. This complicates validation of results and leads to inconsistent visualizations between analysis suites. Conversion between object formats is the most common solution, but this is difficult and error-prone due to the rapid pace of develop

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Showers, William M., Jairav Desai, Krysta L. Engel, Clayton Smith, Craig T. Jordan, and Austin E. Gillen. "SCUBA implements a storage format-agnostic API for single-cell data access in R." F1000Research 13 (June 2, 2025): 1256. https://doi.org/10.12688/f1000research.154675.2.

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While robust tools exist for the analysis of single-cell datasets in both Python and R, interoperability is limited, and analysis tools generally only accept one object class. Considerable programming expertise is required to integrate tools across package ecosystems into a comprehensive analysis, due to their differing languages and internal data structures. This complicates validation of results and leads to inconsistent visualizations between analysis suites. Conversion between object formats is the most common solution, but this is difficult and error-prone due to the rapid pace of develop

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Prof. Vasudha Bahl and Prof. Nidhi Sengar, Akash Kumar, Dr Amita Goel. "Real-Time Object Detection Model." International Journal for Modern Trends in Science and Technology 6, no. 12 (2020): 360–64. http://dx.doi.org/10.46501/ijmtst061267.

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Object Detection is a study in the field of computer vision. An object detection model recognizes objects of the real world present either in a captured image or in real-time video where the object can belong to any class of objects namely humans, animals, objects, etc. This project is an implementation of an algorithm based on object detection called You Only Look Once (YOLO v3). The architecture of yolo model is extremely fast compared to all previous methods. Yolov3 model executes a single neural network to the given image and then divides the image into predetermined bounding boxes. These

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Franconeri, S. L., S. V. Jonathan, and J. M. Scimeca. "Tracking Multiple Objects Is Limited Only by Object Spacing, Not by Speed, Time, or Capacity." Psychological Science 21, no. 7 (2010): 920–25. http://dx.doi.org/10.1177/0956797610373935.

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In dealing with a dynamic world, people have the ability to maintain selective attention on a subset of moving objects in the environment. Performance in such multiple-object tracking is limited by three primary factors—the number of objects that one can track, the speed at which one can track them, and how close together they can be. We argue that this last limit, of object spacing, is the root cause of all performance constraints in multiple-object tracking. In two experiments, we found that as long as the distribution of object spacing is held constant, tracking performance is unaffected by

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Chotikunnan, Phichitphon, Tasawan Puttasakul, Rawiphon Chotikunnan, Benjamas Panomruttanarug, Manas Sangworasil, and Anuchart Srisiriwat. "Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications." Journal of Robotics and Control (JRC) 4, no. 3 (2023): 263–77. http://dx.doi.org/10.18196/jrc.v4i3.17932.

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This study presents a method for enhancing the accuracy of object detection in industrial automation applications using ResNet18-based image segmentation. The objective is to extract object images from the background image accurately and efficiently. The study includes three experiments, RGB to grayscale conversion, single image processing, and dual image processing. The results of the experiments show that dual image processing is superior to both RGB to grayscale conversion and single image processing techniques in accurately identifying object edges, determining CG values, and cutting backg

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Galeano Weber, Elena M., Haley Keglovits, Arin Fisher, and Silvia A. Bunge. "Insights into visual working memory precision at the feature- and object-level from a hemispheric encoding manipulation." Quarterly Journal of Experimental Psychology 73, no. 11 (2020): 1949–68. http://dx.doi.org/10.1177/1747021820934990.

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Mnemonic precision is an important aspect of visual working memory (WM). Here, we probed mechanisms that affect precision for spatial (size) and non-spatial (colour) features of an object, and whether these features are encoded and/or stored separately in WM. We probed precision at the feature-level—that is, whether different features of a single object are represented separately or together in WM—and the object-level—that is, whether different features across a set of sequentially presented objects are represented in the same or different WM stores. By manipulating whether stimuli were encode

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Bölling, Christian, Satpal Bilkhu, Christian Gendreau, Falko Glöckler, James Macklin, and David Shorthouse. "Representation of Object Provenance for Research on Natural Science Objects: Samples, parts and derivatives in DINA-compliant collection data management." Biodiversity Information Science and Standards 6 (September 7, 2022): e94531. https://doi.org/10.3897/biss.6.94531.

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Collection objects in natural science collections span a diverse set of object types of substantially different origin, physical composition, and relevance for different fields and methodologies of research and application. Object provenance is often characterized by elaborate series of interventions from collecting or observing originals in a natural state to generating derived objects that can be physically persistent or are suitable for a given use. This sequence of events gives rise to intermediate objects or object states that can be of a persistent or ephemeral nature in their own right.

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