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Online multi-target intelligent tracking using a deep long-short term memory network

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中国航空学报:英文版 2023年第9期36卷 313-329页

作者： Yongquan ZHANG Zhenyun SHI Hongbing JI Zhenzhen SU School of Electronic Engineering Xidian UniversityXi’an 710071China School of Computer Science and Technology Xidian UniversityXi’an 710071China

multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In this paper,considering the model-free purpose,we present an online multi-target Intelligent tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM algorithm.Firstly,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,respectively.Further,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement sequence.Finally,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and deaths.Experimental results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios.

关键词： Data association Deep long-short term memory network Historical sequence multi-target tracking target tuple set Track management

multi-target tracking of Person Based on Deep Learning

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Computer Systems Science & Engineering 2023年第11期47卷 2671-2688页

作者： Xujun Li Guodong Fang Liming Rao Tengze Zhang Physics and Optoelectronic Engineering College Xiangtan UniversityXiangtan411105China

To improve the tracking accuracy of persons in the surveillance video,we proposed an algorithm for multi-target tracking persons based on deep learning.In this paper,we used You Only Look Once v5(YOLOv5)to obtain person targets of each frame in the video and used Simple Online and Realtime tracking with a Deep Association Metric(DeepSORT)to do cascade matching and Intersection Over Union(IOU)matching of person targets between different frames.To solve the IDSwitch problem caused by the low feature extraction ability of the Re-Identification(ReID)network in the process of cascade matching,we introduced Spatial Relation-aware Global Attention(RGA-S)and Channel Relation-aware Global Attention(RGA-C)attention mechanisms into the network structure.The pre-training weights are loaded for Transfer Learning training on the dataset CUHK03.To enhance the discrimination performance of the network,we proposed a new loss function design method,which introduces the Hard-Negative-Mining way into the benchmark triplet loss.To improve the classification accuracy of the network,we introduced a Label-Smoothing regularization method to the cross-entropy loss.To facilitate the model’s convergence stability and convergence speed at the early training stage and to prevent the model from oscillating around the global optimum due to excessive learning rate at the later stage of training,this paper proposed a learning rate regulation method combining Linear-Warmup and exponential decay.The experimental results on CUHK03 show that the mean Average Precision(mAP)of the improved ReID network is 76.5%.The Top 1 is 42.5%,the Top 5 is 65.4%,and the Top 10 is 74.3%in Cumulative Matching Characteristics(CMC);Compared with the original algorithm,the tracking accuracy of the optimized DeepSORT tracking algorithm is improved by 2.5%,the tracking precision is improved by 3.8%.The number of identity switching is reduced by 25%.The algorithm effectively alleviates the IDSwitch problem,improves the tracking accuracy of persons,and has a high practical v

关键词： YOLOv5 DeepSORT deep learning attention mechanism person re-identification multi-target tracking

An Iterative Pose Estimation Algorithm Based on Epipolar Geometry With Application to multi-target tracking

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 942-953页

作者： Jacob H.White Randal W.Beard the Brigham Young University ProvoUtah 84058 USA

This paper introduces a new algorithm for estimating the relative pose of a moving camera using consecutive frames of a video sequence. State-of-the-art algorithms for calculating the relative pose between two images use matching features to estimate the essential matrix. The essential matrix is then decomposed into the relative rotation and normalized translation between frames. To be robust to noise and feature match outliers, these methods generate a large number of essential matrix hypotheses from randomly selected minimal subsets of feature pairs, and then score these hypotheses on all feature pairs. Alternatively, the algorithm introduced in this paper calculates relative pose hypotheses by directly optimizing the rotation and normalized translation between frames, rather than calculating the essential matrix and then performing the decomposition. The resulting algorithm improves computation time by an order of magnitude. If an inertial measurement unit(IMU) is available, it is used to seed the optimizer, and in addition, we reuse the best hypothesis at each iteration to seed the optimizer thereby reducing the number of relative pose hypotheses that must be generated and scored. These advantages greatly speed up performance and enable the algorithm to run in real-time on low cost embedded hardware. We show application of our algorithm to visual multi-target tracking(MTT) in the presence of parallax and demonstrate its real-time performance on a 640 × 480 video sequence captured on a UAV. Video results are available at https://youtu.be/Hh K-p2 h XNn U.

关键词： Aerial robotics epipolar geometry multi-target tracking pose estimation unmanned aircraft systems vision-based flight

Adaptive resource management for multi-target tracking in co-located MIMO radar based on time-space joint allocation

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Journal of Systems Engineering and Electronics 2020年第5期31卷 916-927页

作者： SU Yang CHENG Ting HE Zishu LI Xi LU Yanxi School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

Compared with the traditional phased array radar, the co-located multiple-input multiple-output(MIMO) radar is able to transmit orthogonal waveforms to form different illuminating modes, providing a larger freedom degree in radar resource management. In order to implement the effective resource management for the co-located MIMO radar in multi-target tracking,this paper proposes a resource management optimization model,where the system resource consumption and the tracking accuracy requirements are considered comprehensively. An adaptive resource management algorithm for the co-located MIMO radar is obtained based on the proposed model, where the sub-array number, sampling period, transmitting energy, beam direction and working mode are adaptively controlled to realize the time-space resource joint allocation. Simulation results demonstrate the superiority of the proposed algorithm. Furthermore, the co-located MIMO radar using the proposed algorithm can satisfy the predetermined tracking accuracy requirements with less comprehensive cost compared with the phased array radar.

关键词： co-located multiple-input multiple-output(MIMO)radar adaptive resource management multi-target tracking sub-array division time-space joint allocation

A new algorithm of bearings-only multi-target tracking of bistatic system

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控制理论与应用（英文版） 2006年第4期4卷 331-337页

作者： Benlian XU Zhiquan WANG Department of Information and Control Engineering Changshu Institute of Technology Changshu Jiangsu 215500 China School of Automation Nanjing University of Science and Technology Nanjing Jiangsu 210094 China

Much research mainly focuses on the batch processing method （e.g. maximum likelihood method） when bearings-only multiple targets tracking of bistatic sonar system is considered. In this paper, the idea of recursive processing method is presented and employed, and corresponding data association algorithms, i.e. a multi-objective ant-colony-based optimization algorithm and an easy fast assignment algorithm are developed to solve the measurements-to-measurements and measurements-to-tracks data association problems of bistatic sonar system, respectively. Monte-Carlo simulations are induced to evaluate the effectiveness of the proposed methods.

关键词： Bearings-only multi-target tracking Data association Ant colony optimization

VIDEO multi-target tracking BASED ON PROBABILISTIC GRAPHICAL MODEL

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Journal of Electronics(China) 2011年第Z1期 548-557页

作者： Xu Feng Huang Chenrong* Wu Zhengjun Xu Lizhong(College of Computer and Information Engineering,Hohai University,Nanjing 210098,China) *(School of Computer Engineering,Nanjing Institute of Technology,Nanjing 211167,China)

In the technique of video multi-target tracking,the common particle filter can not deal well with uncertain relations among multiple targets.To solve this problem,many researchers use data association method to reduce the multi-target uncertainty.However,the traditional data association method is difficult to track accurately when the target is occluded.To remove the occlusion in the video,combined with the theory of data association,this paper adopts the probabilistic graphical model for multi-target modeling and analysis of the targets relationship in the particle filter framework.Ex-perimental results show that the proposed algorithm can solve the occlusion problem better compared with the traditional algorithm.

关键词： Video tracking multi-target tracking Data association Probabilistic graphical model Particle filter

Research and Application of multi-target tracking Based on GM-PHD Filter

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Optics and Photonics Journal 2020年第6期10卷 125-133页

作者： Yanyi Li Limin Guo Xiangsong Huang Harbin Engineering University Harbin China

In recent years, multi-target tracking technology based on Gaussian Mixture- Probability Hypothesis Density (GM-PHD) filtering has become a hot field of information fusion research. This article outlines the generation and development of multi-target tracking methods based on GM-PHD filtering, and the principle and implementation method of GM-PHD filtering are explained, and the application status based on GM-PHD filtering is summarized, and the key issues of the development of GM-PHD filtering technology are analyzed.

关键词： GM-PHD multi-target tracking Random Finite Set

VIDEO multi-target tracking BASED ON PROBABILISTIC GRAPHICAL MODEL

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Journal of Electronics(China) 2011年第4期28卷 548-557页

作者： Xu Feng Huang Chenrong Wu Zhengjun Xu Lizhong College of Computer and Information Engineering Hohai University Nanjing 210098 China School of Computer Engineering Nanjing Institute of Technology Nanjing 211167 China

关键词： Video tracking multi-target tracking Data association Probabilistic graphical model Particle filter

Effective implementation and improvement of fast labeled multi-Bernoulli filter

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Journal of Systems Engineering and Electronics 2023年第3期34卷 661-673页

作者： CHENG Xuan JI Hongbing ZHANG Yongquan School of Electronic Engineering Xidian UniversityXi’an 710071China

Effective implementation of the fast labeled multi-Bernoulli(FLMB)filter is addressed for target tracking with interval measurements.Firstly,a sequential Monte Carlo(SMC)implementation of the FLMB filter,SMC-FLMB filter,is derived based on generalized likelihood function weighting.Then,a box particle(BP)implementation of the FLMB filter,BP-FLMB filter,is developed,with a computational complexity reduction of the SMC-FLMB filter.Finally,an improved version of the BP-FLMB filter,improved BP-FLMB(IBP-FLMB)filter,is proposed,improving its estimation accuracy and real-time performance under the conditions of low detection probability and high clutter.Simulation results show that the BP-FLMB filter has a great improvement of the real-time performance than the SMC-FLMB filter,with similar tracking performance.Compared with the BP-FLMB filter,the IBP-FLMB filter has better estimation performance and real-time performance under the conditions of low detection probability and high clutter.

关键词： multi-target tracking interval measurements fast labeled multi-Bernoulli(FLMB)filter sequential Monte Carlo(SMC)implementation box particle(BP)implementation