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Experimental study of pedestrian behaviors in a corridor based on digital image processing

FIRE SAFETY JOURNAL 2012年 47卷 8-15页

作者： Tian, Wei Song, Weiguo Ma, Jian Fang, Zhiming Seyfried, Armin Liddle, Jack Univ Sci & Technol China State Key Lab Fire Sci Hefei 230026 Peoples R China China Ship Dev & Design Ctr Wuhan 430036 Peoples R China SW Jiaotong Univ Sch Transportat & Logist Chengdu 610031 Peoples R China Forschungszentrum Julich Julich Supercomp Ctr D-52425 Julich Germany

Evacuation experiments of pedestrians passing through a corridor, which acts as a bottleneck, are conducted in this study. A video processing method is used to extract the trajectories of pedestrians in the measurement section. The velocity, density, flow rate and time headway between successive pedestrians entering the bottleneck are calculated, and the relations between them are obtained from the trajectories. We report that the mean velocities for each experiment vary greatly, depending on the lanes contained in the measurement section. The mean velocity of each pedestrian in the corridor displays a trend that decreases sharply at the beginning and then fluctuates slightly around the mean value. A fundamental diagram relating density and speed is obtained. We also investigate the relation between the mean velocities of pedestrians and the density, as a function of time, and report that the mean velocity decreased while the density increased. These results are consistent with those of other researchers. The flow rate increases linearly with the bottleneck width, which is consistent with other results. The time headways are investigated in this study. It is observed that the behavior of pedestrians in the corridor, such as when they formed lanes, has a significant effect on the time headways and their distribution. The distribution of time headways in different lanes is also studied, and some interesting results are obtained. The data and results provided in this paper may be useful for improving evacuation models and designing public facilities. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Evacuation Pedestrian Experiment mean-shift algorithm Behavior

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Extraction and quantitative analysis of microscopic evacuation characteristics based on digital image processing

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2009年第13期388卷 2717-2726页

作者： Liu, Xuan Song, Weiguo Zhang, Jun Univ Sci & Technol China State Key Lab Fire Sci Hefei 230026 Peoples R China

In this Study, experiments of single-file pedestrian movement were Conducted and the movement parameters of pedestrians were extracted with a digital image processing method based on a mean-shift algorithm. The microscopic characteristics of pedestrian dynamics, including velocity, density, and lateral oscillation, as well as their interrelations, were obtained and analyzed. Firstly, we studied the lateral oscillation phenomena of pedestrian movement. The result indicates that the trajectory of pedestrians presents a wavy form and the amplitude of the oscillation remains about 5.5 cm when the pedestrians move with free walking velocity, which is the velocity when there is 110 Obvious interaction between sequential pedestrians;but when the movement velocity decreases to 0.27 m/s, the amplitude of oscillation increases to 13 cm. With increasing density, the velocity decreases and the amplitude of oscillation presents a linear increase trend. The increasing oscillation amplitude widens the Occupation area of a pedestrian with low velocity, so as to make the moving efficiency even worse. Secondly. we Studied the frequency of the oscillation;the result indicates that the frequency remains at 2 Hz when pedestrians move with a free walking velocity, but it presents a similar linear decrease trend when the velocity changes to a lower value. The decrease of oscillation frequency is also a negative feedback to the moving efficiency. Thirdly, it is found that with the increase of crowd density, the time interval between two sequential pedestrians increases, though the space gap between them decreases. The quantitative relation between time interval and crowd density is obtained. The study in this paper provides fundamental data and a basic method for understanding pedestrian dynamics, developing and validating evacuation models. The results are also expected to be useful for evacuation design. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Evacuation Pedestrian flow Digital image processing mean-shift algorithm Experiment

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An extended TLD tracking algorithm using co-training learning for low frame rate videos

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第16期82卷 24743-24769页

作者： Moridvaisi, Hooman Razzazi, Farbod Pourmina, Mohammadali Dousti, Massoud Islamic Azad Univ Dept Elect & Comp Engn Sci & Res Branch Tehran Iran

The conventional tracking-learning-detection (TLD) algorithm is sensitive to illumination changes, clutter, significant changes of target shape between consecutive frames. In addition, low frame rate scenarios result in drift in object position or even missing the object. To solve these problems and enhance the tracking robustness, in this paper, TLD algorithm was extended in two folds. First, Kalman filter and mean-shift algorithms were combined for the tracking part and second, co-training semi-supervised learning algorithm was used for the learning part of the conventional TLD structure. The Kalman filter estimates the position of the target in the next frame based on the previous positions of the target. This reduces tracking failure. On the other hands, the mean-shift tracking algorithm is robust to rotation, partial occlusion and scale changing. In the learning part of TLD structure, two training tracking algorithms with two independent classifiers were run on the current frame simultaneously. Its structure makes data of both pools (color features and target templates) update by the results of other algorithm, in addition to the results of the corresponding algorithm in each of the tracking and detection algorithms. Therefore, classifiers can learn faster changing features of the target during the consecutive frames in online tracking process. Finally, the extended structure can solve the problem of lost object in LFR videos tracking and other similar challenges simultaneously. In terms of overlap ratio metric, comparing with conventional TLD and extended kernelized correlation filters (EKCF) algorithms, the success rate of our algorithm under various scenarios has increased by 161.03% and 255.82% respectively and under other scenarios, in terms of precision metric, it has increased by 18.46% and 1479.47%, respectively. Accordingly, comparative evaluations of the proposed method to other top state-of-the-art tracking algorithms under various scenarios present

关键词： Object tracking Kalman filter mean-shift algorithm Learning algorithm Tracking learning detection

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Including the Size of Regions in Image Segmentation by Region-Based Graph

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2014年第2期23卷 635-644页

作者： Rezvanifar, Alireza Khosravifard, Mohammadali Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

Applying a fast over-segmentation algorithm to image and working on a region-based graph (instead of the pixel-based graph) is an efficient approach to reduce the computational complexity of graph-based image segmentation methods. Nevertheless, some undesirable effects may arise if the conventional cost functions, such as Ncut, AverageCut, and MinCut, are employed for partitioning the region-based graph. This is because these cost functions are generally tailored to pixel-based graphs. In order to resolve this problem, we first introduce a new class of cost functions (containing Ncut and AverageCut) for graph partitioning whose corresponding suboptimal solution can be efficiently computed by solving a generalized eigenvalue problem. Then, among these cost functions, we propose one that considers the size of regions in the partitioning procedure. By simulation, the performance of the proposed cost function is quantitatively compared with that of the Ncut and AverageCut.

关键词： Graph-based image segmentation Ncut AverageCut spectral clustering region-based graph mean-shift algorithm

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Automatic Nuclei Detection Based on Generalized Laplacian of Gaussian Filters

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2017年第3期21卷 826-837页

作者： Xu, Hongming Lu, Cheng Berendt, Richard Jha, Naresh Mandal, Mrinal Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 1H9 Canada Case Western Reserve Univ Dept Biomed Engn Cleveland OH 44106 USA Univ Alberta Cross Canc Inst Dept Oncol Edmonton AB T6G 1Z2 Canada

Efficient and accurate detection of cell nuclei is an important step toward automatic analysis in histopathology. In this work, we present an automatic technique based on generalized Laplacian of Gaussian (gLoG) filter for nuclei detection in digitized histological images. The proposed technique first generates a bank of gLoG kernels with different scales and orientations and then performs convolution between directional gLoG kernels and the candidate image to obtain a set of response maps. The local maxima of response maps are detected and clustered into different groups by mean-shift algorithm based on their geometrical closeness. The point which has the maximum response in each group is finally selected as the nucleus seed. Experimental results on two datasets show that the proposed technique provides a superior performance in nuclei detection compared to existing techniques.

关键词： Laplacian of Gaussian (LoG) filter mean-shift algorithm nuclei detection thresholding

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Adaptive mean-shift Kalman Tracking for Laparoscopic Surgery

Adaptive Mean-Shift Kalman Tracking for Laparoscopic Surgery

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2011 4th IEEE International Conference on Computer Science and Information Technology(ICCSIT 2011)

作者： Vera Sa-Ing Jackrit Suthakorn Saowapak S.Thongvigitmanee Chumpon Wilasrusmee Department of Biomedical Engineering Mahidol University Nakornpathom THAILAND Image Technology Lab National Electronics and Computer Technology Center Pathumthani THAILAND Department of Surgery Ramathibodi Hospital Bangkok THAILAND

In this paper,we propose the adaptive mean-shift Kalman tracking based on the mean-shift algorithm combined with the Kalman filter for tracking the laparoscopic instrument in laparoscopic *** an iterative increment update of the target candidate in the mean-shift process,the proposed algorithm has improved the performance over a typical mean-shift *** addition,the Kalman filter is employed to enhance the chance of tracking accuracy,especially when the object disappears from the *** tested the tracking performance of our proposed algorithm by using different situations from simulated *** all experimental results,the proposed algorithm can locate the target object correctly even when the size and the shape of the target have been ***,in the difficult situation when the target is hiding behind an obstacle,this algorithm can still track the target object correctly after it comes ***,this proposed algorithm can be used for locating the tip of the laparoscopic instrument in real laparoscopic surgery.

关键词： mean-shift algorithm Kalman filter object tracking laparoscopic surgery minimally invasive surgery

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Adaptive Shape Formation Against Swarm-Scale Variants in Robot Swarms

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第8期9卷 6776-6783页

作者： Li, Xing Zhou, Rui Sun, Guibin Beihang Univ Sch Automat Sci & Elect Engn Beijing 100083 Peoples R China

Shape formation of robot swarms is quite challenging when the robot number varies, as the number often needs to match the number of goal locations in the shape. For this challenge, state-of-the-art methods characterize the shape as a continuous region and distribute robots in the region. However, such methods can only handle a small swarm-scale variant due to the fixed shape size. In this letter, we propose a distributed adaptive shape formation method with variable shape size. The core idea is that each robot can dynamically adjust the shape size according to variants of local density, induced by variants of the robot number. Furthermore, this individual adjustment by each robot can be propagated through peer-to-peer communications. In particular, this strategy is integrated into our previous work, which employs the mean-shift algorithm to achieve the shape formation of large-scale robot swarms. In addition, we also adapt the mean-shift algorithm for redesigning all the negotiation and control components in the previous work such that a concise and unified alternative for shape formation can be provided via the mean-shift algorithm. Finally, simulation and experiment results demonstrate that the proposed method can achieve the desired shape with the robot number decreasing or increasing.

关键词： Robots Shape Robot sensing systems Collision avoidance Multi-robot systems Heuristic algorithms Sun Adaptive shape formation variable swarm scale density feedback mean-shift algorithm multi-robot systems

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A nonparametric adaptive tracking algorithm based on multiple feature distributions

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IEEE TRANSACTIONS ON MULTIMEDIA 2006年第6期8卷 1156-1163页

作者： Polat, Ediz Ozden, Mustafa Kirikkale Univ Dept Elect & Elect Engn TR-71451 Kirikkale Turkey

This paper presents an object tracking framework based on the mean-shift algorithm, which is a nonparametric technique that uses statistical color distribution of objects. Tracking objects through highly similar-colored background is one of the problems that need to be addressed. In various cases where object and background color distributions are very similar, the color distribution obtained from single frame alone is not sufficient to track objects reliably. To deal with this problem, the proposed algorithm utilizes an adaptive statistical background and foreground modeling to detect the change due to motion using kernel density estimation techniques based on multiple recent frames. The use of multiple frames supplies more information than single frame and thus it provides more accurate modeling of both background and foreground. In addition to color distribution, this statistical multiple frame-based motion representation is integrated into a modified mean-shift algorithm to create more robust object tracking framework. The use of motion distribution provides additional discriminative power to the framework. The superior performance with quantitative results of the framework has been validated using experiments on synthetic and real sequence of images.

关键词： kernel density estimation mean-shift algorithm object tracking

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Product typicality attribute mining method based on a topic clustering ensemble

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第8期55卷 6629-6654页

作者： Sun, Jing-Tao Zhang, Qiu-Yu Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Network Data Anal & Intelligent P Xian 710121 Shaanxi Peoples R China Xian Key Lab Big Data & Intelligent Comp Xian 710121 Shaanxi Peoples R China Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Gansu Peoples R China

Despite the extensive application of topic models in natural language processing tasks in recent years, the Chinese texts of short comments characterised by large scale, high noise and small information points have put forward higher requirements for the accuracy and stability of the results, which fails to be satisfied by existing topic models. In this paper, a product typicality attribute mining method based on a topic clustering ensemble was proposed. By introducing multiple topic models into ensemble learning, the problems of semantic representation loss, clustering inefficiency and lack of interpretability in the mining of product typicality attributes of short comment texts should be solved. By an effective combination of the topic clustering algorithm based on the diversity of speech, the topic clustering ensemble algorithm based on the Non-negative matrix factorization, and the interpretation method of product typicality attributes based on the mean-shift algorithm, an unsupervised model of product typicality attribute mining for short comment texts is constructed. As shown by the experimental results, the modelling method assumes favourable performance in topic clustering and feature selection, suggesting its advantages in product typicality attribute identification and interpretability compared with common methods.

关键词： LDA models Integrated learning Non-negative matrix factorization mean-shift algorithm

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Experiment and analysis on microscopic characteristics of pedestrian movement in building bottleneck

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Science China(Technological Sciences) 2011年第7期54卷 1730-1736页

作者： TIAN Wei SONG WeiGuo LU Wei FANG ZhiMing State Key Laboratory of Fire Science University of Science and Technology of China Hefei 230027 China

In this paper, evacuation experiments are carried out to study pedestrian movement behaviors in building bottleneck. An image processing method based on mean-shift algorithm is used to extract pedestrian movement trajectory. Based on the extracted trajectory, we analyze the microscopic movement characteristics of pedestrians such as lane formation, change of velocity and distance between two sequential pedestrians. A pedestrian lane is a group of pedestrians moving in a column. The lane formation is verified by the pedestrian trajectory and distribution of pedestrian’s lateral positions (x direction in the paper): lane number changes from one to two, three or even more with the increasing bottleneck width when pedestrians pass through the bottleneck. By analyzing the pedestrian movement behaviors in the same pedestrian lane, we find three typical movement modes in the bottleneck: time-lag acceleration, synchronous acceleration, and avoiding deceleration. Through analyzing the time intervals when successive pedestrians pass through the bottleneck, we find that most pedestrians adjust their velocities according to the distance to the forward pedestrians. Results also indicate that due to different cultures, pedestrians flux in China and Germany may have some differences besides their similarities.

关键词： evacuation trajectory mean-shift algorithm pedestrian lane time interval

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