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11th International Conference on Distributed Smart Cameras (ICDSC)

作者： Abdali, El Mehdi Hanniche, Abderrahmane Walid Pelcat, Maxime Diguet, Jean-Philippe Berry, Francois Inst Pascal UMR CNRS 6602 Aubiere France Univ Clermont Auvergne Aubiere France INSA Rennes IETR UMR CNRS 6164 Rennes France UMR CNRS 3192 Lab STICC Lorient France

ISBN: (纸本)9781450354875

Robust object tracking is a crucial enabler of many computer vision applications. An embedded object tracker must, in addition to respecting real-time requirements, robustly update the position of the tracked object in the captured images and be able to re-identify the object when it reappears after disappearing for a number of frames. To obtain such a robustness, modern algorithms such as the tracking learning detection (TLD) combine classical tracking techniques with learning capabilities. This combination makes it possible to track an object's displacement, learn its new appearances when it changes orientation and re-identify it once it reappears. The strong performances of these techniques are obtained using costly computation that challenges hardware capabilities in the context of embedded architectures. This paper proposes a strategy for porting the TLD algorithm on an Field-Programmable Gate Array (FPGA). Based on parallel image resizing and selective object patch rejection, the proposed dataflow-oriented strategy demonstrates how the structure of the algorithm can be adapted to be more streaming-oriented and fit the structure of an FPGA. The results prove a tracking performance of up to 160 fps under 50Mhz for VGA images while keeping nearly the tracking robustness of the reference algorithm implementation.

关键词： FPGA Image processing tracking learning detection Dataflow

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Intelligent Robot Target detection Algorithm Integrating TLD-GOTURN

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IEEE ACCESS 2024年 12卷 83439-83451页

作者： Zhou, Li Liu, Yan Hunan City Univ Sch Mech & Elect Engn Yiyang 413001 Peoples R China Hunan City Univ Coll Informat & Elect Engn Yiyang 413001 Peoples R China

The development of intelligent robot has always been an important research direction in the field of artificial intelligence, and the object detection of robot is the basis of intelligent perception and autonomous action. This study proposes an improved object detection algorithm which integrates two kinds of intelligent robot object detection techniques. In this process, the relationship between the center position of the target in the frame used for object detection and tracking is analyzed. It uses Laplace probability density as the parameter to calculate the center position relationship, and joins the correction network to correct the feature point positioning. The object re-capture function is added to solve the problem of object loss in the long-term object detection task, and the classifier is used to realize the object recognition. The results show that when the number of targets in the image reaches 20, the capture accuracy of the two data sets remains above 98.7%. In the intersection to union ratio test, when the real rectangular box contained 2M pixels, the intersection to union ratio of the method proposed in this study remains at or above 0.989. When conducting actual application memory usage tests, the proposed method maintains a memory usage of less than 2000Mb at runtime. It is shown that this method has better target detection efficiency and quality, and the requirement of hardware is lower.

关键词： Object detection generic object tracking using regression networks tracking learning detection intelligent robots Laplace probability density

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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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Correlation tracking with Correcting and Adaptive Update Strategy

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2019年第5期14卷 2181-2190页

作者： Peng, Shao-Hu Hyun-Do Nam Guangzhou Univ Sch Mech & Elect Engn 230 GuangZhou Univ City Outer Ring Rd Guangzhou Guangdong Peoples R China Dankook Univ Dept Elect & Elect Engn Yongin Gyeonggi Do South Korea

Object tracking plays an important role in the research field of computer vision. Correlation filter (CF) based tracking algorithms have shown remarkable performance recently. However, there are two problems: (1) the online model is prone to drift due to the fixed coefficient update strategy;(2) a tracking error is susceptible to lead the failure of the following tracking task due to the absence of a correcting strategy. To deal with these limitations, we proposed a new correlation tracking filter that includes an adaptive update strategy and a correcting strategy. The adaptive update strategy is based on the confident degree of the tracking result, which can minimize the effect of image noise. And the correcting strategy is based on a four-level classifier that can enhance the error correcting ability. Based on these two strategies, the proposed CCAS not only can improve the accuracy of the correlation tracking, but also can build a detector with strong error correcting ability to handle a variety of challenges. Experiments show the proposed method has the effective correcting ability and can resist model drift. It is noted that the proposed algorithm not only outperforms other state-of-art algorithms but also runs enough fast for real time application.

关键词： Object tracking Correlation filter tracking learning detection Four-level classifier Computer vision

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A novel rotation invariant and Manhattan metric-based pose refinement: Augmented reality-based oral and maxillofacial surgery

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INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY 2020年第3期16卷 e2077-e2077页

作者： Bayrak, Mucahit Alsadoon, Abeer Prasad, P. W. C. Venkata, Haritha Sallepalli Ali, Rasha S. Haddad, Sami Charles Sturt Univ Sch Comp & Math Sydney NSW Australia AL Nisour Univ Coll Dept Comp Tech Engn Baghdad Iraq Greater Western Sydney Area Hlth Serv Dept Oral & Maxillofacial Serv Mt Druitt NSW Australia Cent Coast Area Hlth Dept Oral & Maxillofacial Serv Gosford NSW Australia

Background Augmented reality (AR) is gaining attention in medicine because of the convenience and innovation that it brings to operating rooms. Furthermore, oral and maxillofacial surgery (OMS), which is one of sensitive and narrow spatial surgery, requires high accuracy in image registration and low processing time of the system. However, the current systems are suffering from image registration problems while matching two different posture images. We thus aimed to increase that overlay accuracy and decrease the processing time. Methodology The proposed system consists of an Iterative Closest Point (ICP) algorithm, which is the combination of a rotation invariant and Manhattan error metric, to provide the best initial parameters and to decrease the computational cost by sorting high and low processing pixel images, respectively. Result The study on maxillary and mandibular jaw bone demonstrates that the proposed work overlay accuracy ranges from 0.22 to 0.30 mm, and processing time ranges from 10 to 14 frames per second as opposed to the 0.23- to 0.35-mm overlay accuracy and the current 8 to 12 frames per second processing time. Conclusion This research aimed to improve the visualization and fast AR system for the OMS. Thus, the proposed system achieved an improvement in overlay accuracy and processing time by implementing the Rotation Invariant and Manhattan error metric ICP algorithm.

关键词： augmented reality image registration Iterative Closest Point jaw surgery rotation invariant tracking learning detection

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Human motion tracking: A comparative study 2

Human motion tracking: A comparative study

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2nd International Conference on Intelligent Computing in Data Sciences (ICDS)

作者： Mrabti, Wafae Baibai, Kaoutar Bellach, Benaissa Thami, Rachid Oulad Haj Tairi, Hamid Univ Sidi Mohammed Ben Abdellah FSDM Dept Informat LIIAN Fes Morocco First Mohammed Univ Natl Sch Appl Sci LSE2I Oujda Morocco Mohammed V Univ Fac Sci Rabat IT Ctr LRIT Rabat Morocco

In this paper, we aim to carry out a brief study of a new tracking system and the results of existing tracking systems including Compressive tracking (CT), Fragment Tracker (FRAG), Multiple Instance learning Tracker (MIL), and tracking learning detection algorithm (TLD). For this purpose, we present the principles of those tracking systems. Then, we test them on several datasets that contain challenging attributions. Finally, we evaluate them qualitatively and quantitatively by two widely used criterions: the Average Center Error (ACE) and the Average Overlap Error (AOE). We conclude our paper by discussing the obtained results and the future works in this field. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： tracking human being Compressive tracking Fragment Tracker Multiple Instance learning tracking learning detection Predicting learning tracking

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Human motion tracking: A comparative study

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Procedia Computer Science 2019年 148卷 145-153页

作者： Wafae Mrabti Kaoutar Baibai Benaissa Bellach Rachid Oulad Haj Thami Hamid Tairi LIIAN Department of Informatics FSDM University of Sidi Mohammed Ben Abdellah Fez Morroco LSE2I National School of Applied Sciences First Mohammed University Oujda Morroco LRIT Rabat IT CenterFaculty of Sciences Mohammed V University Rabat Morocco

关键词： tracking human being Compressive tracking Fragment Tracker Multiple Instance learning tracking learning detection Predicting learning tracking

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A Novel Real-Time Fall detection System Based on Real-Time Video and Mobile Phones

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2017年第4期26卷 1750056-1750056页

作者： Tong, Chao Lian, Yu Zhang, Yang Xie, Zhongyu Long, Xiang Niu, Jianwei Beihang Univ Sch Comp Sci Xueyuan Rd 37 Beijing 100191 Peoples R China

In recent years, due to the growing population of the elderly, falls of elderly people have aroused wide public concern. Detecting timely falls of the elderly is significant to their safety. Numerous challenges exist in real-time fall detection systems because some features of normal human activities are greatly similar to the characteristics of falls. To address these problems, we propose a novel fall detection scheme and build a health-care system to detect falls of the elderly based on a real-time video surveillance system and a smart phone. The system contains two major modules. The first module is a feature extraction module. We adopt the Gaussian mixture model, tracking learning detecting algorithm and logpolar histogram to extract the characteristics of falls from the video surveillance system and the sensors embedded in mobile phones. The main purpose of the second module is to detect a fall-based on the features obtained in the first module. The experimental results show that every module is significant. Besides, our system is effective to separate falls from other similar actions such as bend down with an accuracy rate of more than 98% and performs better than other state-of-the-art fall detection systems.

关键词： Health-care system fall detection head tracking tracking learning detection mobile sensors

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Micro-Expression Recognition by Feature Points tracking

Micro-Expression Recognition by Feature Points Tracking

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10th International Conference on Communications (COMM)

作者： Yao, Shuoqing He, Ning Zhang, Huiquan Yoshie, Osamu Waseda Univ Grad Sch Informat Prod & Syst Wakamatsu Ku 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan Waseda Univ Res Ctr Informat Prod & Syst Fukuoka Japan

ISBN: (纸本)9781479923854

One of the most interesting aspects of facial expression analysis is recognizing micro-expression. In this paper, a new feature tracking and alignment approach for micro-expression based on FACS systems and tracking learning detection(TLD) is presented. The basic point for detecting first frame feature point is based on Hough Forest, and in order to increase the accuracy, we extracted features by Local Binary Pattern(LBP) as initialization. Unlike many previous works, the proposed approach applies conceptual area in perspective of human cognition. And this approach aims to track the extracted features and quantifies changing trend of these points for analyzing micro-expression. To estimate our approach's rationality, we conducted experiments on the CASME and SMIC facial expression database. The results show that the proposed approach is effective and performs well in recognizing some specific micro-expressions. Furthermore, the proposed approach is more accurate than previous methods based on Temporal Interpolation Model(TIM).

关键词： Micro-Expression tracking learning detection Hough Forest Local Binary Pattern Temporal Interpolation Model

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The Incremental PCA tracking With Negative Samples

The Incremental PCA Tracking With Negative Samples

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IEEE International Conference on Consumer Electronics - China

作者： Qing, Chunmei Zhao, Simin Xu, Xiangmin South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China

ISBN: (纸本)9781479947560

Object tracking is a difficult task in computer vision, which is usually affected by color, surrounding illumination, variation of the object's appearance and other factors. In previous years, many algorithms can only set up fixed appearance models to track object. Recently, more and more tracking algorithms have been proposed to deal with object appearance variation and illumination change. However, these algorithms are easily influenced by the background and can only track the object for a short time. A novel incremental principal component algorithm with classifier detection is proposed to solve the drifting and long-term tracking problems. Numerous experiments demonstrate that the proposed algorithm is more robust than several state-of-the-art algorithms.

关键词： Visual tracking incremental subspace learning tracking learning detection drifting

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