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Multiple human tracking in drone image

MULTIMEDIA TOOLS AND APPLICATIONS 2019年第4期78卷 4563-4577页

作者： Hai Duong Nguyen Na, In Seop Kim, Soo Hyung Lee, Guee Sang Yang, Hyung Jeong Choi, Jun Ho Chonnam Natl Univ Sch Elect & Comp Engn Gwangju South Korea Chosun Univ Software Convergence Educ Inst Gwangju South Korea Chosun Univ Div Undeclared Majors Gwangju South Korea

Object tracking, especially human tracking is one of the challenging research problems in computer vision. Although the performance has gained some positive changes recently, there is still room for improvement. In this paper, we introduce an approach for human detection and tracking using Convolution Neural Network (CNN) and hungarian algorithm (HA). A CNN is used to localize multiple human beings from frame to frame in a video stream. This deep CNN is known as Faster R-CNN which achieved the state of the art performance in object detection problem. In the tracking process, we solve the data association problem in visual tracking using HA. A detected person will be assigned to a tracklet based on the data distribution in the video frame. The experimental results show that our system can deal with the videos captured from different scenarios in near real-time.

关键词： Drone images Faster R-CNN Human tracking hungarian algorithm

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Multichannel wireless scheduling under limited terminal capability

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2008年第2期7卷 611-617页

作者： Choi, Young-June Bahk, Saewoong Seoul Natl Univ Dept Elect Engn & Comp Sci Seoul 151742 South Korea NEC Lab Amer Princeton NJ 08540 USA

Emerging systems like OFDMA and MIMO systems require multichannel scheduling over a wireless link. In this paper, we focus on the case that the number of channels to be assigned to a mobile terminal is limited, which we call limited matching. This case often occurs over a MIMO downlink when the number of receive antennas at a mobile user is smaller than that of transmit antennas at the base station. When the system exploits channel-aware opportunistic scheduling based on the channel feedback, finding the sum of channel gains by the limited matching is an NP-complete problem. To solve it easily, we can use the hungarian algorithm, but its complexity is still too high and not amenable to performance analysis., Hence, we develop a heuristic algorithm, and analyze its cell throughput. Also, we investigate its performance when the channel feedback information is partially available. For analytic simplicity, we consider proportionally fair (PF) scheduling that is widely accepted as an opportunistic scheduler. Numerical results demonstrate that our heuristic limited-matching scheduling algorithm works well with partial channel feedback.

关键词： wireless networks multichannel scheduling opportunistic scheduling hungarian algorithm channel feedback performance analysis

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Polynomial-Time Assignment-Based Cell Association with Generic Utility Functions

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China Communications 2022年第9期19卷 214-228页

作者： Lusheng Wang Chao Fang Hai Lin Min Peng Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Cell association is a significant research issue in future mobile communication systems due to the unacceptably large computational time of traditional *** article proposes a polynomial-time cell association scheme which not only completes the association in polynomial time but also fits for a generic optimization objective *** the one hand,traditional cell association as a non-deterministic polynomial(NP)hard problem with a generic utility function is heuristically transformed into a 2-dimensional assignment optimization and solved by a certain polynomial-time algorithm,which significantly saves computational *** the other hand,the scheme jointly considers utility maximization and load balancing among multiple base stations(BSs)by maintaining an experience pool storing a set of weighting factor values and their corresponding *** an association optimization is required,a suitable weighting factor value is taken from the pool to calculate a long square utility matrix and a certain polynomial-time algorithm will be applied for the *** with several representative schemes,the proposed scheme achieves large system capacity and high fairness within a relatively short computational time.

关键词： mobile communication system 2-dimensional assignment problem hungarian algorithm fairness cell association

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Improving Switch-to-Controller Assignment with Load Balancing in Multi-controller Software Defined WAN (SD-WAN)

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2020年第3期28卷 553-575页

作者： El Kamel, Ali Youssef, Habib Univ Sousse PRINCE Lab Sousse Tunisia CCK Manouba Tunisia

Software defined networking (SDN) gains a lot of interest from network operators due to its ability to offer flexibility, efficiency and fine-grained control over forwarding elements (FE) by decoupling control and data planes. In the control plane, a centralized node, denoted controller, receives requests from ingress switches and makes decisions on path forwarding. Unfortunately, requests processing may lead to controller performance degradation as the number of incoming requests goes up. This paper deals with the controller performance issue in Software Defined WAN (SD-WAN). Mainly, it proposes a new approach to optimize the switch-to-controller assignment problem with load balancing support. The issue is formulated as a Minimum Cost Bipartite Assignment optimization problem which is solved using an improvement of the hungarian algorithm. The new algorithm is based on the introduction of the load-driven penalty concept which aims to achieve a trade-off between the round trip time and the controller load. Finally, a new protocol denoted Distributed hungarian-based Assignment Protocol (DHAP) is described as an implementation of the proposed solution in multi-controller environments. As shown in results, the proposed solution outperforms parallel schemes in terms of flow setup time and load balancing.

关键词： SDN hungarian algorithm AOP Linear assignment Bipartite graphs

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Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR

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REMOTE SENSING 2024年第6期16卷

作者： Li, Yu Shi, Xiaoran Wang, Xiaoning Lu, Yongqiang Cheng, Peipei Zhou, Feng Xidian Univ Key Lab Elect Informat Countermeasure & Simulat Te Minist Educ Xian 710071 Peoples R China State Key Lab Astronaut Dynam Xian 710043 Peoples R China

In complex electromagnetic environments, satellite telemetry, tracking, and command (TT&C) signals often become submerged in background noise. Traditional TT&C signal detection algorithms suffer a significant performance degradation or can even be difficult to execute when phase information is absent. Currently, deep-learning-based detection algorithms often rely on expert-experience-driven post-processing steps, failing to achieve end-to-end signal detection. To address the aforementioned limitations of existing algorithms, we propose an intelligent satellite TT&C signal detection method based on triplet attention and Transformer (TATR). TATR introduces the residual triplet attention (ResTA) backbone network, which effectively combines spectral feature channels, frequency, and amplitude dimensions almost without introducing additional parameters. In signal detection, TATR employs a multi-head self-attention mechanism to effectively address the long-range dependency issue in spectral information. Moreover, the prediction-box-matching module based on the hungarian algorithm eliminates the need for non-maximum suppression (NMS) post-processing steps, transforming the signal detection problem into a set prediction problem and enabling parallel output of the detection results. TATR combines the global attention capability of ResTA with the local self-attention capability of Transformer. Experimental results demonstrate that utilizing only the signal spectrum amplitude information, TATR achieves accurate detection of weak TT&C signals with signal-to-noise ratios (SNRs) of -15 dB and above (mAP@0.5 > 90%), with parameter estimation errors below 3%, which outperforms typical target detection methods.

关键词： satellite TT&C signal detection complex electromagnetic environment attention mechanism Transformer hungarian algorithm

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A framework for simulation-based optimization of business process models

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2012年第7期88卷 852-869页

作者： Kamrani, Farzad Ayani, Rassul Moradi, Farshad KTH Royal Inst Technol Sch Informat & Commun Technol Stockholm Sweden Swedish Def Res Agcy Div Informat Syst FOI Stockholm Sweden

The Assignment Problem is a classical problem in the field of combinatorial optimization, having a wide range of applications in a variety of contexts. In general terms, the Assignment Problem consists of determining the best assignment of tasks to agents according to a predefined objective function. Different variants of the Assignment Problem have been extensively investigated in the literature in the last 50 years. In this work, we introduce and analyze the problem of optimizing a business process model with the objective of finding the most beneficial assignment of tasks to agents. Despite similarities, this problem is distinguished from the traditional Assignment Problem in that we consider tasks to be part of a business process model, being interconnected according to defined rules and constraints. In other words, assigning a business process to agents is a more complex form of the Assignment Problem. Two main categories of business processes, assignment-independent and assignment-dependent, are distinguished. In the first category, different assignments of tasks to agents do not affect the flow of the business process, while processes in the second category contain critical tasks that may change the workflow, depending on who performs them. In each category several types of processes are studied. algorithms for finding optimal and near-optimal solutions to these categories are presented. For the first category, depending on the type of process, the hungarian algorithm is combined with either the analytical method or simulation to provide an optimal solution. For the second category, we introduce two algorithms. The first one finds an optimal solution, but is feasible only when the number of critical tasks is small. The second algorithm is applicable to large number of critical tasks, but provides a near-optimal solution. In the second algorithm a hill-climbing heuristic method is combined with the hungarian algorithm and simulation to find an overall near-opt

关键词： Assignment Problem business process optimization hungarian algorithm simulation-based optimization

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Registering sets of points using Bayesian regression

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NEUROCOMPUTING 2012年 89卷 122-133页

作者： Gerogiannis, Demetrios Nikou, Christophoros Likas, Aristidis Univ Ioannina Dept Comp Sci GR-45110 Ioannina Greece

This work addresses the problem of non-rigid registration between two 2D or 3D points sets as a novel application of Relevance Vector Machines (RVM). An iterative framework is proposed which consists of two steps: at first, correspondences between distinct points are estimated by the hungarian algorithm and then a regression procedure based on a Bayesian linear model (RVM) maps the two sets of points. By these means, a large variety of transformation is captured without imposing any prior knowledge on the form of the point sets. The proposed algorithm provides a smooth transformation even if the correspondence between the points in the two sets contains erroneous matches. The algorithm was successfully evaluated on sets of points with varying difficulty and favorably compared with state-of-the-art methods in cases of noise. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Registration of sets of points Relevance vector machine (RVM) hungarian algorithm

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Point cloud registration with quantile assignment

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MACHINE VISION AND APPLICATIONS 2024年第3期35卷 1-17页

作者： Oguz, Ecenur Dogan, Yalim Gudukbay, Ugur Karasan, Oya Pinar, Mustafa Bilkent Univ Dept Ind Engn TR-06800 Ankara Turkiye Bilkent Univ Dept Comp Engn TR-06800 Ankara Turkiye

Point cloud registration is a fundamental problem in computer vision. The problem encompasses critical tasks such as feature estimation, correspondence matching, and transformation estimation. The point cloud registration problem can be cast as a quantile matching problem. We refined the quantile assignment algorithm by integrating prevalent feature descriptors and transformation estimation methods to enhance the correspondence between the source and target point clouds. We evaluated the performances of these descriptors and methods with our approach through controlled experiments on a dataset we constructed using well-known 3D models. This systematic investigation led us to identify the most suitable methods for complementing our approach. Subsequently, we devised a new end-to-end, coarse-to-fine pairwise point cloud registration framework. Finally, we tested our framework on indoor and outdoor benchmark datasets and compared our results with state-of-the-art point cloud registration methods.

关键词： Point cloud registration Fast point feature histograms (FPFH) descriptor Quantile assignment Iterative closest point algorithm Bipartite graph matching hungarian algorithm Hopcroft-Karp algorithm

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Artificial potential field-empowered dynamic holographic optical tweezers for particle-array assembly and transformation

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PhotoniX 2024年第1期5卷 167-183页

作者： Xing Li Yanlong Yang Shaohui Yan Wenyu Gao Yuan Zhou Xianghua Yu Chen Bai Dan Dan Xiaohao Xu Baoli Yao State Key Laboratory of Transient Optics and Photonics Xi’an Institute of Optics and Precision MechanicsChinese Academy of SciencesXi’an 710119China University of Chinese Academy of Sciences Beijing 100049China

Owing to the ability to parallel manipulate micro-objects,dynamic holographic optical tweezers(HOTs)are widely used for assembly and patterning of particles or ***,for simultaneous control of large-scale targets,potential collisions could lead to defects in the formed *** we introduce the artificial potential field(APF)to develop dynamic HOTs that enable collision-avoidance *** eliminating collision risks among particles,this method can maximize the degree of parallelism in multi-particle transport,and it permits the implementation of the hungarian algorithm for matching the particles with their target sites in a minimal *** proof-of-concept experiments,we employ APF-empowered dynamic HOTs to achieve direct assembly of a defect-free 8×8 array of microbeads,which starts from random initial *** further demonstrate successive flexible transformations of a 7×7 microbead array,by regulating its tilt angle and inter-particle spacing distances with a minimalist *** anticipate that the proposed method will become a versatile tool to open up new possibilities for parallel optical micromanipulation tasks in a variety of fields.

关键词： Dynamic holographic optical tweezers Path planning hungarian algorithm Artificial potential field

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Pedestrian Group Re-Identification and Trajectory Prediction Through Zone-Based Clustering

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IEEE ACCESS 2024年 12卷 101549-101562页

作者： Chen, Mingzuoyang Banitaan, Shadi Maleki, Mina Univ Detroit Mercy Dept Elect & Comp Engn & Comp Sci Detroit MI 48221 USA

Pedestrian trajectory prediction is a critical aspect of computer vision, aimed at predicting a pedestrian's future locations by analyzing their past movements. Traditional trajectory prediction models primarily focus on individuals, which can be challenging in densely populated areas due to occlusions. These occlusions not only complicate the re-identification of pedestrians once they reappear but also increase processing times due to the more complex procedures and the greater number of objects involved. However, a common observation is that pedestrians often travel in groups. This insight led us to propose a novel approach, predicting the future trajectories of pedestrian groups instead of individuals. This strategy effectively addresses the complexities of predicting movements in crowded environments and the issues related to pedestrian occlusion. In this work, we introduced a distinctive methodology for identifying pedestrian groups, re-identifying them, and predicting their future trajectories. Our approach, unlike traditional state-of-the-art re-identification and trajectory prediction methods of individual pedestrians, focuses on re-identifying pedestrian groups and predicting their future trajectories while emphasizing processing time reduction with great accuracy. The process started with object detection to ascertain pedestrian coordinates. Subsequently, a zone-based clustering method was employed to form groups. Following this, a specific group re-identification was utilized to construct continuous trajectories for these groups, rather than for individual pedestrians. Finally, the group trajectory prediction technique was applied to estimate the future movements of these groups. Both the object detection and group detection methods were applied every five frames to generate these trajectories. The effectiveness of our approach has been validated using several evaluation metrics, including Average Displacement Error (ADE), Final Displacement Error (FDE),

关键词： Trajectory Pedestrians Accuracy Predictive models Feature extraction Object detection Long short term memory Cameras Computer vision Identification of persons hungarian algorithm human view camera LSTM re-identification trajectory prediction zone-based group detection

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