A maneuvering target tracking based on fastIMM-extended Viterbi algorithm

Di, Yi; Li, Ruiheng; Tian, Hao; Guo, Jia; Shi, Binghua; Wang, Zheng; Yan, Ke; Liu, Yueheng

doi:10.1007/s00521-023-09039-1

Cited by 28 publications

(6 citation statements)

References 21 publications

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“…This algorithm is mainly aimed at extracting trajectories from massive motion data, but its essence is an object detection method. Therefore, this algorithm can be applied to other fields related to motion data, such as autonomous driving [31], robot navigation [32], intelligent monitoring [33], etc. In the future, this algorithm can be used for the detection and tracking of vehicles, pedestrians, or other moving targets in these fields, further achieving tasks such as path planning and behavior analysis.…”

Section: Discussionmentioning

confidence: 99%

Design of Reliable Mining Algorithm for Massive Moving Image Data Trajectory

Zeng,

Run,

Djenouri

2024

IEEE Access

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To acquire precise, dependable, and credible trajectory information from extensive motion image datasets, this study introduces a robust mining algorithm grounded in trajectory extraction for voluminous motion data. The algorithm leverages an enhanced single-stage object detection model (TFF-SSD) and employs a 3D sparse convolutional neural network for extracting point cloud features from the extensive motion data. Simultaneously, spatial semantic features are derived by integrating spatial and semantic characteristics of the vast motion data. Subsequently, a mutual attention fusion method is applied to amalgamate point cloud features and spatial semantic features into the Faster R-CNN model. This facilitates the swift identification of moving target regions, leading to the extraction of classification and coordinate information from the abundant data. Employing reference points from diverse types of extensive motion data, the algorithm selects associated feature regions for the motion data target, thereby obtaining reference point information indicative of continuous motion. Subsequent fitting of this reference point information enables the reliable mining of trajectories within the extensive motion data. Experimental outcomes demonstrate the algorithm's ability to accurately detect all crowd movements in a running race. The extracted motion data features are diverse, encompassing reference points that facilitate trajectory mining with elevated quality, reliability, and genuine data content.

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Section: Discussionmentioning

confidence: 99%

Design of Reliable Mining Algorithm for Massive Moving Image Data Trajectory

Zeng,

Run,

Djenouri

2024

IEEE Access

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“…Kurtosis ( Lu et al, 2023 ; Liu et al, 2023b ; Liu et al, 2023c ; Liu et al, 2023d ) on the other hand, measures the tailedness of the distribution. Higher kurtosis indicates a more extreme result, meaning that more of the variance is the result of infrequent extreme deviations, as opposed to frequent modestly sized deviations ( Miao et al, 2023 ; Di et al, 2023 ; Ahmad et al, 2020 ; Liu et al, 2023e ). The mathematical equation for kurtosis is

…”

Section: Methodsmentioning

confidence: 99%

Robust human locomotion and localization activity recognition over multisensory

Khan,

Alonazi,

Abdelhaq

et al. 2024

Front. Physiol.

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Human activity recognition (HAR) plays a pivotal role in various domains, including healthcare, sports, robotics, and security. With the growing popularity of wearable devices, particularly Inertial Measurement Units (IMUs) and Ambient sensors, researchers and engineers have sought to take advantage of these advances to accurately and efficiently detect and classify human activities. This research paper presents an advanced methodology for human activity and localization recognition, utilizing smartphone IMU, Ambient, GPS, and Audio sensor data from two public benchmark datasets: the Opportunity dataset and the Extrasensory dataset. The Opportunity dataset was collected from 12 subjects participating in a range of daily activities, and it captures data from various body-worn and object-associated sensors. The Extrasensory dataset features data from 60 participants, including thousands of data samples from smartphone and smartwatch sensors, labeled with a wide array of human activities. Our study incorporates novel feature extraction techniques for signal, GPS, and audio sensor data. Specifically, for localization, GPS, audio, and IMU sensors are utilized, while IMU and Ambient sensors are employed for locomotion activity recognition. To achieve accurate activity classification, state-of-the-art deep learning techniques, such as convolutional neural networks (CNN) and long short-term memory (LSTM), have been explored. For indoor/outdoor activities, CNNs are applied, while LSTMs are utilized for locomotion activity recognition. The proposed system has been evaluated using the k-fold cross-validation method, achieving accuracy rates of 97% and 89% for locomotion activity over the Opportunity and Extrasensory datasets, respectively, and 96% for indoor/outdoor activity over the Extrasensory dataset. These results highlight the efficiency of our methodology in accurately detecting various human activities, showing its potential for real-world applications. Moreover, the research paper introduces a hybrid system that combines machine learning and deep learning features, enhancing activity recognition performance by leveraging the strengths of both approaches.

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“…The authors in [ 25 ] used an auto-encoder as a label. A deep-learning classification model [ 26 ] was trained on the KDD data set, which achieved an average accuracy of 85%.…”

Section: Background Studymentioning

confidence: 99%

“…The proposed neural network design can improve prediction accuracy in the following scenarios. On three benchmark data sets, they examined rival intrusion detection architectures [ 35 , 36 ]. In the review [ 37 ], the PSO-Xgboost model is introduced because it outperforms competing models like Xgboost, Irregular Backwoods, Stowing, and Adaboost regarding overall order precision.…”

Section: Background Studymentioning

confidence: 99%

Advancements in intrusion detection: A lightweight hybrid RNN-RF model

Khan,

Mohmand,

Rehman

et al. 2024

PLoS ONE

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Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats.

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A maneuvering target tracking based on fastIMM-extended Viterbi algorithm

Cited by 28 publications

References 21 publications

Design of Reliable Mining Algorithm for Massive Moving Image Data Trajectory

Design of Reliable Mining Algorithm for Massive Moving Image Data Trajectory

Robust human locomotion and localization activity recognition over multisensory

Advancements in intrusion detection: A lightweight hybrid RNN-RF model

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