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作者： Pavlík, Vít Brno University of Technology

Diplomová práce se zabývá dlouhodobým sledováním objektů ve videosekvencích. Cílem práce je demonstrovat techniky potřebné ke zvládnutí dlouhodobéh... 详细信息

Diplomová práce se zabývá dlouhodobým sledováním objektů ve videosekvencích. Cílem práce je demonstrovat techniky potřebné ke zvládnutí dlouhodobého sledování objektů, především pak ty jejichž aplikace vede k vytvoření adaptivního sledovacího systému, který dokáže vhodně reagovat na změnu vzhledu objektu zájmu a nestálou povahu okolního prostředí.

关键词： dlouhodobé sledování objektu sledování detekcí online učení detektorů strukturované učení podpůrné vektory regresní analýza vážené nejmenší čtverce jádrové funkce long-term object tracking tracking by detection online learning detectors structu

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Weighted correlation filters guidance with spatial-temporal attention for online multi-object tracking

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2019年 63卷 102576-000页

作者： Tian, Sheng Zou, Lian Fan, Cian Chen, Liqiong Wuhan Univ Elect Informat Sch Wuhan Hubei Peoples R China

In recent years, discriminative correlation filters based trackers have made remarkable achievements for single object tracking, while directly applying these trackers for multi-object tracking may encounter some problem in drifted results caused by occlusion and missing detection from the detector. Thus, we propose a weighted-correlation-filters framework with spatial-temporal attention mechanism for online multi-object tracking to solve the above problems. First, we use the weighted correlation filters with dynamic updating scheme to pre-track each object in the current frame, which helps to filter out the improper detection according to the position of pre-tack for each object and is capable of tracking objects of the false negative. Then, we introduce a spatial-temporal attention mechanism to produce a discriminative appearance model and calculate reliable similarity scores for data association. The proposed online algorithm achieves 48.4% in MOTA on challenging MOT17 benchmark dataset and better performance on MT and ML than some offline methods. (C) 2019 Published by Elsevier Inc.

关键词： Multi-object tracking Weighted correlation filters tracking by detection Spatial-temporal attention mechanism

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Automatic multi-plaque tracking and segmentation in ultrasonic videos

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MEDICAL IMAGE ANALYSIS 2021年 74卷 102201-102201页

作者： Li, Leyin Hu, Zhaoyu Huang, Yunqian Zhu, Wenqian Wang, Yuanyuan Chen, Man Yu, Jinhua Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Jiao Tong Univ Tongren Hosp Dept Ultrasound Shanghai Peoples R China

Carotid plaque tracking and segmentation in ultrasound videos is the premise for subsequent plaque property evaluation and treatment plan development. However, the task is quite challenging, as it needs to address the problems of poor image quality, plaque shape variations among frames, the existence of multiple plaques, etc. To overcome these challenges, we propose a new automatic multi-plaque tracking and segmentation (AMPTS) framework. AMPTS consists of three modules. The first module is a multi object detector, in which a Dual Attention U-Net is proposed to detect multiple plaques and vessels simultaneously. The second module is a set of single-object trackers that can utilize the previous tracking results efficiently and achieve stable tracking of the current target by using channel attention and a ranking strategy. To make the first module and the second module work together, a parallel tracking module based on a simplified 'tracking-by-detection' mechanism is proposed to solve the challenge of tracking object variation. Extensive experiments are conducted to compare the proposed method with several state-of-the-art deep learning based methods. The experimental results demonstrate that the proposed method has high accuracy and generalizability with a Dice similarity coefficient of 0.83 which is 0.16, 0.06 and 0.27 greater than MAST (Lai et al., 2020), Track R-CNN (Voigtlaender et al., 2019) and VSD (Yang et al., 2019) respectively and has made significant improvements on seven other indicators. In the additional Testing set 2, our method achieved a Dice similarity coefficient of 0.80, an accuracy of 0.79, a precision of 0.91, a Recall 0.70, a F1 score of 0.79, an AP@0.5 of 0.92, an AP@0.7 of 0.74, and an expected average overlap of 0.79. Numerous ablation studies suggest the effectiveness of each proposed component and the great potential for multiple carotid plaques tracking and segmentation in clinical practice. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Carotid plaque Ultrasound videos tracking by detection Multi-object tracking Deep learning

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tracking Human Poses with Head Orientation Estimation

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Instrumentation 2017年第3期4卷 40-46页

作者： TIAN Jinglan WANG Zhengyuan LI Ling LIU Wanquan School of Information Science and Engineering University of Jinan Jinan Tengyue Electronic Co.Ltd. Department of Computing Curtin University

Lots of progress has been made recently on 2 D human pose tracking with tracking-by-detection approaches. However,several challenges still remain in this area which is due to self-occlusions and the confusion between the left and right limbs during tracking. In this work,a head orientation detection step is introduced into the tracking framework to serve as a complementary tool to assist human pose estimation. With the face orientation determined,the system can decide whether the left or right side of the human body is exactly visible and infer the state of the symmetric counterpart. By granting a higher priority for the completely visible side,the system can avoid double counting to a great extent when inferring body poses. The proposed framework is evaluated on the HumanEva dataset. The results show that it largely reduces the occurrence of double counting and distinguishes the left and right sides consistently.

关键词： Human Pose tracking Head Orientation tracking by detection

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MULTIPLE OBJECT tracking BASED ON SPARSE GENERATIVE APPEARANCE MODELING

MULTIPLE OBJECT TRACKING BASED ON SPARSE GENERATIVE APPEARAN...

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IEEE International Conference on Image Processing

作者： Dorra Riahi Guillaume-Alexandre Bilodeau LITIV Lab. Polytechnique Montreal

ISBN: (纸本)9781479983407

This paper addresses multiple object tracking which still remains a challenging problem because of factors like frequent occlusions, unknown number of targets and similarity in objects' appearance. We propose a novel approach for multiple object tracking using a multiple feature framework. The main focus of the proposed method is to build a robust appearance model. The appearance model of an object is built using a color model, a sparse appearance model, a motion model and spatial information. We validated the proposed algorithm on four publicly available videos with comparisons with state-of-the-art approaches. We demonstrate that our algorithm achieves competitive results.

关键词： Multiple object tracking Data association tracking by detection Sparse appearance model

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A Keypoint-based Fast Object tracking Algorithm

A Keypoint-based Fast Object Tracking Algorithm

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第35届中国控制会议

作者： Weihua Cao Qiang Ling Feng Li Quan Zheng Song Wang University of Science and Technology of China

ISBN: (纸本)9781509009107

This paper proposes a novel keypoint-based algorithm to track multiple objects in real time from a fixed *** of searching keypoints from the whole video image,we only detect keypoints from the region of interest,which can be obtained by adaptive background mixture *** approach is robust against object appearance changes because of its learning ***,our algorithm makes a good use of fast keypoint detectors and binary descriptors to run in realtime,which qualifies it for online *** requires no camera or ground plane calibration and can efficiently work at a frame rate of 28-32 fps under the image resolution of 768×576.

关键词： Real-time tracking by detection Keypoint-based algorithms

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DOUBLE LAYER SALIENT PARTS BASED MULTI-PEOPLE tracking

DOUBLE LAYER SALIENT PARTS BASED MULTI-PEOPLE TRACKING

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IEEE International Conference on Image Processing

作者： Zhi Zhou Yue Wang Eam Khwang Teoh Nanyang Technological University School of Electrical and Electronic Engineering Institute for Infocomm Research (I2R) Visual Computing Department

ISBN: (纸本)9781479983407

Multi-people tracking has been studied for decades. It is already applied in many computer vision tasks. In this paper, the proposed framework performs frame to frame tracking and follows tracking-by-detection approach. Saliency detection is introduced to enhance multi-people tracking. It is performed on two layers in this method. Salient parts inside the human patch denote representative regions of the target, while parts around the target capture context information. Short term tracking of salient parts is applied along with data association. When data association with detections fails, the supporting models on-line learned are used to indicate the locations of targets based on the tracking results of salient parts. A Bayesian based method is used for mutual occlusion reasoning. Experiments are carried out on several public datasets to evaluate the proposed method. The experimental results show the promising performance of the proposed method compared with state-of-the-art works.

关键词： Multi-people tracking Saliency detection tracking by detection Context information tracking data association Targets PART TWO-LAYER DOUBLE images computer vision TARGET ACQUISITION

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OCCLUSION-AWARE HMM-BASED tracking BY LEARNING

OCCLUSION-AWARE HMM-BASED TRACKING BY LEARNING

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IEEE International Conference on Image Processing

作者： Tughan Marpuc A. Aydin Alatan Aselsan Inc. Department of Electrical and Electronics Engineering METU

ISBN: (纸本)9781479957521

Recently, an emerging class of methods, namely tracking by detection, achieved quite promising results on challenging tracking data sets. These techniques train a classifier in an online manner to separate the object from its background. These methods only take input location of the object and a random feature pool;then, a classifier bootstraps itself by using the current tracker state and extracted positive and negative samples. Following these approaches, a novel tracking system is proposed. A feature selection method is introduced to increase the discriminative power of the classifier. During tracking, a Hidden Markov Model (HMM) is utilized to filter the features that improve the performance. Moreover, a state of the proposed HMM is allocated to handle occlusions. The proposed tracker is tested on publicly available challenging video sequences and superior tracking results are achieved in real-time.

关键词： tracking by detection Discriminative methods Hidden Markov Models Occlusion handling

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Collaborative Detector Fusion of Data-Driven PHD Filter for Online Multiple Human tracking

Collaborative Detector Fusion of Data-Driven PHD Filter for ...

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International Conference on Information Fusion

作者： Zeyu Fu Syed Mohsen Naqvi Jonathon A. Chambers ISC Research Group School of Engineering Newcastle University UK Department of Engineering University of Leicester UK

The use of multiple data sources (measurements) has been recently demonstrated to improve the accuracy and reliability of a tracking system as it is capable of providing redundancy in different aspects, and also eliminating interferences of individual sources. This paper focuses on addressing the multiple human tracking problem from a multi-detector approach. This approach integrates two detectors with different characteristics (full-body and body-parts) to perform robust collaborative fusion based on data-driven Gaussian Mixture Probability Hypothesis Density (GM-PHD) filters. To leverage the maximum strengths from multiple detectors, we propose a robust fusion center at the track level, which manages to perform Generalized Intersection Covariance (GCI) fusions for survival and birth tracks independently, and also eliminates false tracks caused by a cluttered environment Moreover, an identity reassignment mechanism is also developed to address the identity mismatching problem in the target birth process, so as to enhance the fusion performance and track consistency. Experimental results on two challenging benchmark video sequences confirm the effectiveness of the proposed approach.

关键词： Multiple human tracking tracking by detection GM-PHD filter Data driven technique Multi-detector fusion

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