Multi-object Tracking Method Based on Efficient Channel Attention and Switchable Atrous Convolution

Xiang, Xuezhi; Ren, Wenkai; Qiu, Yujian; Zhang, Kaixu; Lv, Ning

doi:10.1007/s11063-021-10519-5

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…Compared with MOT_GM and CRF_RNN methods, the multi-object tracking precision is improved by 4.9% and 6.8%, respectively. On the MOT17 dataset, the accuracy of multi-object tracking is improved by 11.3%, 0.6%, 1.0%, 21.2%, and 2.3%, respectively, compared with JDE, FairMOT, MOT_ES [25], CRF_RNN, and LMOT methods. Compared with JDE method and CRF_RNN method, IDF1 is increased by 10.8% and 16.6%, respectively.…”

Section: Performance Comparison Experiments With Existing Methodsmentioning

confidence: 99%

Research on Pedestrian Multi-Object Tracking Network Based on Multi-Order Semantic Fusion

Liu,

Han

2023

WEVJ

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Aiming at the problem of insufficient tracking accuracy caused by object occlusion in the process of multi-object tracking, this paper proposes a multi-order semantic fusion pedestrian multi-object tracking network. Firstly, the feature pyramid attention module is used in the backbone network to enlarge the receptive field and obtain more abundant feature information to improve the detection accuracy of different scale objects. Secondly, a size-aware module is integrated into the pedestrian re-identification branch network to fuse semantic features from different resolutions and extract more basic pedestrian features, thereby improving the tracking accuracy. Finally, the detection head is reconstructed and the small object detection layer is fused to make the proposed network adapt to objects of different sizes. Experiments on the MOT16 and MOT17 datasets show that the multi-object tracking accuracy of the proposed network reaches 75.4% (MOT16) and 74.3% (MOT17), which effectively deals with the problem of low tracking accuracy caused by occlusion in the field of autonomous driving, and achieves good tracking results. The network proposed in this paper improves the tracking accuracy of pedestrians and provides a basis for further practical applications.

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Section: Performance Comparison Experiments With Existing Methodsmentioning

confidence: 99%

Research on Pedestrian Multi-Object Tracking Network Based on Multi-Order Semantic Fusion

Liu,

Han

2023

WEVJ

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“…Xiang et al [42] developed an efficient channel attention and switchable atrous convolution for MOT. The channel attention approach was employed to extract the data in images.…”

Section: Literature Surveymentioning

confidence: 99%

ResNet50-deep affinity network for object detection and tracking in videos

2024

IJATEE

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Multiple-object tracking (MOT) plays a crucial role in addressing many fundamental challenges within the fields of computer vision and video analysis. The majority of MOT methods rely on two primary processes: object detection and data association. Initially, each video frame is analyzed to detect objects, followed by a subsequent step that establishes correlations among the detected objects across multiple frames to generate their tracks. However, the data association for tracking often relies on manually defined criteria such as motion, appearance, grouping, and spatial proximity, among others. In the study, the ResNet50-deep affinity network (DAN) was introduced, which had been designed for the detection and tracking of objects in videos, including those that appear and disappear between frames. The proposed method was evaluated using the widely recognized MOT17 dataset to address MOT challenges. During the preprocessing phase, photometric distortion correction, frame expansion, and cropping were performed. The ResNet50 model was utilized to extract features. The DAN was employed to identify object appearances in video frames and to calculate their cross-frame affinities (CFA). The approach was compared with existing research, including DAN, ByteTrack, the graph neural network for simultaneous detection and tracking (GSDT), the reptile search optimization algorithm with deep learning-based multiple object detection and tracking (RSOADL-MODT), the center graph network (CGTracker), the hybrid motion model, FlowNet2-deep learning, and the super chained tracker (SCT), to validate the efficiency of theResNet50-DAN method. The ResNet50-DAN method achieved superior results, with a multiple-object tracking accuracy (MOTA) of 84.2%, an F1 score for identification metrics (IDF1) of 80.3%, 10,352 false positives (FP), and 1,284 identity switches (ID-Sw). The ResNet50-DAN method demonstrated higher MOTA compared to the existing approaches, including DAN, ByteTrack, GSDT, RSOADL-MODT, CGTracker, the hybrid motion model, FlowNet2-DL, and SCT.

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“…To address this, Wang et al [16] introduced an efficient channel attention (ECA) module in CVPR 2020, surpassing traditional attention methods by circumventing the detriments of dimensionality reduction on model prediction. Additionally, [17] unveiled a multi-objective tracking approach that seamlessly integrates the efficient channel attention (ECA) module into the backbone network. This augmentation adeptly extracts salient information from images, thereby enhancing object detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Optimizing and Analyzing Performance of Motor Fault Diagnosis Algorithms for Autonomous Vehicles via Cross-Domain Data Fusion

Xie,

Li,

Fan

et al. 2023

Processes

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Electric motors play a pivotal role in the functioning of autonomous vehicles, necessitating accurate fault diagnosis to ensure vehicle safety and reliability. In this paper, a novel motor fault diagnosis approach grounded in vibration signals to enhance fault detection performance is presented. The method involves capturing vibration signals from the motor across various operational states and frequencies using vibration sensors. Subsequently, the signals undergo transformation into frequency domain representations through fast Fourier transform. This includes normalizing and concatenating the amplitude frequency and phase frequency signals into comprehensive frequency domain information. Leveraging Gramian image-encoding attributes, cross-domain fusion of time-domain and frequency-domain data is achieved. Finally, the fused Gram angle field map is fed into the ConvMixer deep learning model, augmented by the ECA mechanism to facilitate precise motor fault identification. Experimental outcomes underscore the efficacy of cross-domain data fusion, showcasing improved pattern recognition and recognition rates for the models compared to traditional time-domain methods. Additionally, a comparative analysis of various deep learning models highlights the superior performance of the ECA-ConvMixer model. This study makes significant contributions by introducing a cross-domain data fusion method, merging time-domain and frequency-domain information to enhance motor vibration signal analysis. Additionally, the incorporation of the ECA-ConvMixer deep learning model, equipped with attention mechanisms, effectively captures critical features, thus serving as a robust tool for motor fault diagnosis. These innovations not only enhance diagnostic accuracy but also have broad applications in areas like autonomous vehicles and industry, leading to reduced maintenance expenses and enhanced equipment reliability.

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Multi-object Tracking Method Based on Efficient Channel Attention and Switchable Atrous Convolution

Cited by 7 publications

References 42 publications

Research on Pedestrian Multi-Object Tracking Network Based on Multi-Order Semantic Fusion

Research on Pedestrian Multi-Object Tracking Network Based on Multi-Order Semantic Fusion

ResNet50-deep affinity network for object detection and tracking in videos

Optimizing and Analyzing Performance of Motor Fault Diagnosis Algorithms for Autonomous Vehicles via Cross-Domain Data Fusion

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