Online multi-object tracking with pedestrian re-identification and occlusion processing

Zhang, Xueqin; Wang, Xiaoxiao; Gu, Chunhua

doi:10.1007/s00371-020-01854-0

Cited by 29 publications

(10 citation statements)

References 35 publications

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“…where O u refers to the number of frames and I k refers to the corresponding specific motion of a specific number of frames during motion. e calculation Equation is as follows (3).…”

Section: Reconstruction Of Multimodal 3d Motion Model Of Dynamic Huma...mentioning

confidence: 99%

“…e development of machine vision is inseparable from the wide application of Artificial Intelligence (AI) technology, and at present, the tracking and recognition of motion targets is a hotspot in the research on target tracking [1,2]. Relevant researchers worldwide have made some achievements in the research of this field, which have been applied to various other fields [3]. For instance, in robot vision, the camera can be used to recognize objects; thus, finally, the target object can be grasped [4].…”

Section: Introductionmentioning

confidence: 99%

“…(2) e target tracking optimization function is constructed by using the camera gesture and other parameters of the keyframe to improve the tracking accuracy of human motion gesture. (3) In the process of motion recognition, in order to ensure the effect of deep learning and reduce the corresponding training cost, CNN is introduced after convolutional computation and pool computation to reduce the computational complexity of multimodal training samples and improve the algorithm performance.…”

Section: Introductionmentioning

confidence: 99%

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Gesture Tracking and Recognition Algorithm for Dynamic Human Motion Using Multimodal Deep Learning

Xia

Xing

2022

Security and Communication Networks

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To address the problems of the traditional human motion gesture tracking and recognition methods, such as poor tracking effect, low recognition accuracy, high frame loss rate, and long-time cost, a dynamic human motion gesture tracking and recognition algorithm using multimode deep learning was proposed. Firstly, the collected human motion images are repaired in the three-dimensional (3D) environment, and the multimodal 3D human motion model is reconstructed using the processed images. Secondly, according to the results of model reconstruction, the camera gesture and other parameters of the keyframe are used to construct the target tracking optimization function so as to achieve the purpose of accurate tracking of human motion. Finally, for multimodal human motion gesture learning, a convolutional neural network (CNN) is developed. The trained CNN is utilized to complete dynamic human motion recognition after convolutional and pooling calculations. The results demonstrate that the proposed algorithm is effective in tracking human motion gestures. The average recognition accuracy is 96%, the average frame loss rate is 8.8%, the time cost is low, and the proposed algorithm has a high F-measure and much lower power consumption than other algorithms, indicating that the proposed algorithm is effective.

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“…where O u refers to the number of frames and I k refers to the corresponding specific motion of a specific number of frames during motion. e calculation Equation is as follows (3).…”

Section: Reconstruction Of Multimodal 3d Motion Model Of Dynamic Huma...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gesture Tracking and Recognition Algorithm for Dynamic Human Motion Using Multimodal Deep Learning

Xia

Xing

2022

Security and Communication Networks

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“…Utilization of 3D physical structure and colour information along with a multi-channel colour shape descriptor proposed in [ 46 ] works as a physical blob detector to detect humans. Occlusion is another factor that affects the performance in the realm of multi-object tracking, as observed in [ 49 ].…”

Section: Related Workmentioning

confidence: 99%

A paced multi-stage block-wise approach for object detection in thermal images

2022

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The growing advocacy of thermal imagery in applications, such as autonomous vehicles, surveillance, and COVID-19 detection, necessitates accurate object detection frameworks for the thermal domain. Conventional methods could fall short, especially in situations with poor lighting, for instance, detection during night-time. In this paper, we propose a paced multi-stage block-wise framework for effectively detecting objects from thermal images. Our approach utilizes the pre-existing knowledge of deep neural network-based object detectors trained on large-scale natural image data to enhance performance in the thermal domain constructively. The employed, multi-stage approach drives our model to achieve higher accuracies. And the introduction of the pace parameter during domain adaption enables efficient training. Our experimental results demonstrate that the framework outperforms previous benchmarks on the FLIR ADAS dataset on the person, bicycle, and car categories. We have also illustrated further analysis of the framework, such as the effect of its components on accuracy and training efficiency, its generalizability to other thermal datasets, and its superior performance on night-time images in contrast to state-of-the-art RGB object detectors.

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“…For example, in 1995, Nowlan et al [85] implemented the first tracking system that tracks hand gestures in a sequence of frames using a CNN model. Multi-object tracking (MOT) has been extensively explored in recent literature for a wide range of applications [86,138]. Indeed, MOT (tracking-by-detection) is another aspect of the generic object tracking task.…”

Section: Visual Trackingmentioning

confidence: 99%

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

et al. 2021

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The research progress in multimodal learning has grown rapidly over the last decade in several areas, especially in computer vision. The growing potential of multimodal data streams and deep learning algorithms has contributed to the increasing universality of deep multimodal learning. This involves the development of models capable of processing and analyzing the multimodal information uniformly. Unstructured real-world data can inherently take many forms, also known as modalities, often including visual and textual content. Extracting relevant patterns from this kind of data is still a motivating goal for researchers in deep learning. In this paper, we seek to improve the understanding of key concepts and algorithms of deep multimodal learning for the computer vision community by exploring how to generate deep models that consider the integration and combination of heterogeneous visual cues across sensory modalities. In particular, we summarize six perspectives from the current literature on deep multimodal learning, namely: multimodal data representation, multimodal fusion (i.e., both traditional and deep learning-based schemes), multitask learning, multimodal alignment, multimodal transfer learning, and zero-shot learning. We also survey current multimodal applications and present a collection of benchmark datasets for solving problems in various vision domains. Finally, we highlight the limitations and challenges of deep multimodal learning and provide insights and directions for future research.

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Online multi-object tracking with pedestrian re-identification and occlusion processing

Cited by 29 publications

References 35 publications

Gesture Tracking and Recognition Algorithm for Dynamic Human Motion Using Multimodal Deep Learning

Gesture Tracking and Recognition Algorithm for Dynamic Human Motion Using Multimodal Deep Learning

A paced multi-stage block-wise approach for object detection in thermal images

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

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