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multi object tracking in soccer video focusing on occlusion detection and resolving

multiMEDIA TOOLS AND APPLICATIONS 2023年第23期82卷 35913-35947页

作者： Sani, Mehran Rastegar Sadjad Univ Technol Comp & Informat Technol Dept Mashhad Iran

Today, due to the growth of data and the development of receiving and storing technologies, large datasets have been created in various fields, such as soccer video datasets. Since obtaining the information manually from large datasets is very difficult, an automated system to capture important information from soccer videos is strongly needed. Automated analysis of soccer videos includes many applications such as: analyzing team tactics, confirming referees' decisions, summarizing videos, *** this paper, a forward-backward algorithm is proposed to increase the performance of player detection and tracking. The purpose of this algorithm is to identify and resolve the occlusions among the players and improve the preprocessing steps (playfield extraction and field lines elimination). We also proposed a new method for each preprocessing step to improve the performance of the tracking system. The evaluations show that our tracking algorithm has performed better than previous methods (89% locally and 78% globally).

关键词： multi object tracking Occlusion resolving Field line detection Playfield extraction Player detection

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multi object tracking: A Survey 13

Multi Object Tracking: A Survey

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13th International Conference on Digital Image Processing (ICDIP)

作者： Bouraya, Sara Belangour, Abdessamad Hassan II Univ Lab Informat Technol & Modeling Fac Sci Ben MSik Casablanca Morocco

ISBN: (数字)9781510646018

ISBN: (纸本)9781510646018

multiple target tracking (MTT) or multiple object tracking (MOT), which is a key stage operation for many computer vision applications, has relied and it relies on detecting and identifying targets within videos. The objects of these videos may be, for instance, pedestrians, vehicles or animals regardless of the number of targets or their appearance. The main goal of multi object tracking is to follow paths or trajectories of multiple targets in a sequence. multiple object tracking is a field of computer vision that has a wide range of applications, starting from video surveillance and human computer interaction to robotics. The aim of visual object tracking is to maintain objects' identities. Developing an effective and accurate visual tracking system is very challenging due to the enormous number of problems, such as Illumination variation or background, the famous Clutters meaning the overlap between objects. Low resolution may be due to cameras problems, scale variation, occlusion, and change of targets' positions within video. These challenges have a wide range of approaches and methods in this evolving research area with several algorithms in each subfield. This paper covers the crucial research area for multiple object tracking (MOT) and this study will help researchers accomplish their scientific projects relying on the wide range of algorithms mentioned on this review.

关键词： Computer vision multi object tracking multi target tracking online multiple object tracking tracking by detection visual tracking

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Probability hypothesis density filter using determinantal point processes for multi object tracking

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COMPUTER VISION AND IMAGE UNDERSTANDING 2019年 183卷 33-41页

作者： Jorquera, Felipe Hernandez, Sergio Vergara, Diego Univ Catolica Maule Lab Proc Informac Geoespacial Av San Miguel 3605 Talca Chile

multi object tracking (MOT) has many applications such as video surveillance and event recognition among others. In this paper, we present a novel multi object tracking method using the Probability Density Hypothesis (PHD) filter and Determinantal Point Processes (DPP). The PHD filter is an algorithm for jointly estimating an unknown number of targets and their states from a sequence of observations in the presence of data association uncertainty, noise and false alarms. A tractable implementation of the PHD filter is based on a Gaussian Mixture approximation. However, the Gaussian Mixture PHD suffers from computational problems due to an increasing number of Gaussian components as time progresses. In this paper, we propose a novel pruning method based on Determinantal Point Process which handles the overestimation problem on the number of tracks. The DPP-PHD filter promotes diversity in the resulting Gaussian components and leads to improved tracking results.

关键词： multi object tracking Determinantal Point Processes Gaussian Mixture Probability Hypothesis Density Filter

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Two Layer Network Flow for Fast Data Association on multi object tracking 10

Two Layer Network Flow for Fast Data Association on Multi Ob...

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10th International Conference on Advanced Computer Science and Information Systems (ICACSIS)

作者： Abdillah, Bariqi Jati, Grafika Jatmiko, Wisnu Nurhadiyatna, Adi Univ Indonesia Fac Comp Sci Depok Indonesia Univ Zagreb Trg Marsala Tita 14 Zagreb 10000 Croatia

ISBN: (纸本)9781728101354

multi object tracking is one interesting topics of computer science that has many applications, such as surveillance system, navigation robot, sports analysis, autonomous driving car, and others. One of the main task of multi-object tracking is data association. This study discusses data association on multi-object tracking and its completion with a two-layer network flow approach. Notice that each object on a frame as a node, then there is an edge connecting each node from one frame to other frame and then finding for the set of edges that provide the greatest probability of transition from one frame to the next, or in the optimization problem better known as max-cost network flow. The probability calculation is based on position distance and similarity feature between objects. The data used in this research is 2DMOT2015. The proposed method obtains highly competitive MOTA of 20.1% compared to existing method with fast computation speed by 215.8 fps

关键词： multi object tracking Data Association Convolutional Neural Network Network Flow

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Scalable Real-Time multi object tracking for Automated Driving Using Intelligent Infrastructure

Scalable Real-Time Multi Object Tracking for Automated Drivi...

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Thomsen, Fabian Ortmann, Maximilian Eckstein, Lutz Institute for Automotive Engineering Rwth Aachen University Aachen Germany

ISBN: (纸本)9798350327816

Automated driving highly relies on object detection and tracking. While vehicle autonomous solutions represent the state of the art, connected systems are increasingly being developed and researched. Many current approaches to multi object tracking focus on the accuracy, whereas such a system working as the backbone of a live and offline digital twin of a test field mainly needs to be scalable to a large amount of perceived objects and still real-time capable. This paper presents a novel approach to multi object tracking that can reach framerates of up to 604 Hz spanning a digital test field of 0.5 km2 size with still reasonable accuracy on simulated data. The approach can also be applied to real world data where our framework reaches a similar accuracy as the common baseline AB3DMOT on the nuScenes dataset, being 588 % faster. Using a new motion model in combination with an Unscented Kalman Filter, we can further increase the framework's accuracy and robustness while still reaching a framerate of 544 Hz on nuScenes and 367 Hz on the ACCorD test field. © 2023 IEEE.

关键词： Automated Driving Intelligent Infrastructure Kalman Filter multi object tracking Parallel Computing

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Interactive multi-Scale Fusion of 2D and 3D Features for multi-object Vehicle tracking

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第10期24卷 10618-10627页

作者： Wang, Guangming Peng, Chensheng Gu, Yingying Zhang, Jinpeng Wang, Hesheng Shanghai Jiao Tong Univ Shanghai Engn Res Ctr Intelligent Control & Manage Key Lab Marine Intelligent Equipment & Syst Dept AutomatKey Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Beijing Inst Control Engn Space Optoelect Measurement & Percept Lab Beijing 100190 Peoples R China China Aerosp Sci & Ind Corp X Lab Acad 2 Beijing 100854 Peoples R China

multiple object tracking (MOT) is a significant task in autonomous driving. Nonetheless, relying on one single sensor is not robust enough, because one modality tends to fail in some challenging situations. Texture information from RGB cameras and 3D structure information from Light Detection and Ranging (LiDAR) have respective advantages under different circumstances. Therefore, feature fusion from multiple modalities contributes to the learning of discriminative features. However, it is nontrivial to achieve effective feature fusion due to the completely distinct information modality. Previous fusion methods usually fuse the top-level features after the backbones extract the features from different modalities. The feature fusion happens solely once, which limits the information interaction between different modalities. In this paper, we propose multiscale interactive query and fusion between pixel-wise and point-wise features to obtain more discriminative features. In addition, an attention mechanism is utilized to conduct soft feature fusion between multiple pixels and points to avoid inaccurate match problems of previous single pixel-point fusion methods. We introduce PointNet++ to obtain multi-scale deep representations of point clouds and make it adaptive to our proposed interactive feature fusion between multi-scale features of images and point clouds. Through the interaction module, each modality can integrate more complementary information from the other modality. Besides, we explore the effectiveness of pre-training on each single modality and fine-tuning on the fusion-based model. Our method can achieve 90.32% MOTA and 72.44% HOTA on the KITTI benchmark and outperform other approaches without using multi-scale soft feature fusion.

关键词： multi object tracking 3D point clouds feature fusion computer vision deep learning

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GPU Acceleration of multi-object tracking with Motion Vector interpolation and Affine Transformation 34

GPU Acceleration of Multi-object Tracking with Motion Vector...

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34th IEEE International Conference on Application-Specific Systems, Architectures and Processors (ASAP)

作者： Kunimoto, Yoshiki Chang, Qiong Yamaguchi, Yoshiki Maruyama, Tsutomu Univ Tsukuba Grad Sch Sci & Technol Tsukuba Japan Tokyo Inst Technol Sch Comp Tokyo Japan Univ Tsukuba Fac Syst & Informat Engn Tsukuba Japan

ISBN: (纸本)9798350346855

In recent studies of object detection and tracking, neural networks have been widely used, and their accuracy has improved. However, its computational complexity is very high and requires the use of high-end GPUs. In order to achieve real-time inference on edge devices, it is necessary to reduce the computational complexity of the network by scaling it down, but this leads to a loss of accuracy. To avoid this loss of accuracy, a method has been proposed in which object detection is performed using a neural network at regular intervals, and in the frames in between, the detected object positions are interpolated using motion prediction. In this research, we propose a method to improve the accuracy of interpolation even when the camera is moving by using an affine transformation used for image stabilization. We also show its real-time computation method on Jetson TX2, one of the lowest power embedded GPUs. The proposed method enables real-time processing of object detection using Yolov5s and tracking of the detected objects at the edge.

关键词： multi object tracking acceleration embedded GPU

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multi-object tracking Hierarchically in Visual Data Taken From Drones 17

Multi-Object Tracking Hierarchically in Visual Data Taken Fr...

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IEEE/CVF International Conference on Computer Vision (ICCV)

作者： Pan, Siyang Tong, Zhihang Zhao, Yanyun Zhao, Zhicheng Su, Fei Zhuang, Bojin Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China Ping Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781728150239

Visual understanding tasks on the drone platform have gained considerable attention recently due to the rapid development of drones. In this paper, we present a hierarchical multi-target tracker (HMTT) for visual data taken from drones. Our approach is specifically directed against sequences shot from drone's view with several stages hierarchically performed. The detector detects objects taken from different viewing angles and the detections are filtered to ensure the correctness. Moreover, we propose a method to locate the frames in the case of camera's fast move by two-norm of the homography matrix. Based on that, performance on multi-object tracking is improved with the involvement of Single object tracking and a re-identification subnet. Our method participated in the multi-object tracking Challenge (Task 4) of VisDrone2019 benchmark and achieved state-of-the-art performance.

关键词： Drone captured sequences multi object tracking

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MotionFormer: An Improved Transformer-Based Architecture for multi-object tracking 1

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8th International Conference on Computer Vision and Image Processing (CVIP)

作者： Agrawal, Harshit Halder, Agrya Chattopadhyay, Pratik Indian Inst Technol BHU Varanasi Uttar Pradesh India

ISBN: (数字)9783031585357

ISBN: (纸本)9783031585340;9783031585357

multi-object tracking (MOT) is a crucial task in Computer Vision with numerous real-world applications. In recent years, Transformer-based models have shown promising results in MOT. However, existing methods still face challenges in scenarios involving short-term object occlusion, camera motion, and ambiguous detection especially in low frame-rate videos. In this work, we propose a novel variant of the TrackFormer model that addresses these limitations by integrating an online motion prediction module based on the Kalman Filter that incorporates the important temporal information present in the videos. The addition of the Kalman Filter helps the model study the tracked pedestrians' motion patterns and leverage them for effective association among targets across the video frames even in the case of short-term occlusions without adding much to the computational complexity of the overall framework. The proposed model is termed MotionFormer due to its inherent ability to utilize long-term motion information. Through extensive evaluations using popular tracking datasets, namely MOT-17 and MOT-20, we demonstrate the effectiveness of our approach over existing approaches. Results show that the MotionFormer provides reasonably good tracking accuracy with a much lesser identity switching rate as compared to the other models. Further, it significantly outperforms the base TrackFormer model in terms of tracking accuracy, F1-score, as well as identity switching rate for MOT-17 private and public and MOT-20 private data.

关键词： multi object tracking Computer Vision Motion Prediction MotionFormer Kalman Filter

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A Comparative Study on multi-object tracking Methods for Sports Events 19

A Comparative Study on Multi-object Tracking Methods for Spo...

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19th International Conference on Advanced Communication Technology (ICACT)

作者： Moon, Sungwon Lee, Jiwon Nam, Dowon Kim, Howon Kim, Wonjun ETRI Sportainment Sect SW Content Reserch Lab Daejeon South Korea ETRI Daejeon South Korea ETRI Sportainment Res Sect Daejeon South Korea

ISBN: (纸本)9788996865094

Due to the rapid growth of machine learning technology, there is a need for research to automatically recognize objects and analyze their behavior in various fields, as is the case with sports. Currently, a system for detecting and tracking multiple objects in a sporting event is not accurate enough. Since most of the services depend on the manual operation of an experienced operator, it is necessary to develop a real time tracking technique for detecting the position of an object. In this paper, we propose an algorithm for multi - object tracking in a sporting event by presenting the results of comparing the performance of existing algorithms for multi-object tracking.

关键词： multi object tracking Visual tracking Sports events Video processing KCF

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