Person Re-Identification by Discriminative Local Features of Overlapping Stripes

Fawad, Fawad; Khan, Muhammad Jamil; Rahman, MuhibUr

doi:10.3390/sym12040647

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…Vision-based frameworks employ computer vision algorithms on the images captured by mounted cameras serving in indoor environments. Vision-based methods detect the desired object in non-overlapping camera networks of buildings by classifying the robust visual features extracted from the region of interest [ 26 ]. Vision-based methodologies are highly precise but computationally expensive.…”

Section: Related Workmentioning

confidence: 99%

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

Khattak

Fawad

Nasralla

et al. 2022

Sensors

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Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. Indoor positioning system (IPS) research has attained robust location-based services in complex indoor structures. Unforeseeable propagation loss in complex indoor environments results in poor localization accuracy of the system. Various IPSs have been developed based on fingerprinting to precisely locate an object even in the presence of indoor artifacts such as multipath and unpredictable radio propagation losses. However, such methods are deleteriously affected by the vulnerability of fingerprint matching frameworks. In this paper, we propose a novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data. BoF calculates the vocabulary set using k-mean clustering, where the frequency of the vocabulary in the raw fingerprint data represents the robust final features that improve localization accuracy. Experimental results from simulation-based indoor scenarios and real-time experiments demonstrate that the proposed framework outperforms previously developed models.

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Section: Related Workmentioning

confidence: 99%

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

Khattak

Fawad

Nasralla

et al. 2022

Sensors

Self Cite

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“…In our study, we utilized some existing ReID models, such as CTL-Model [33], DLFOS [34]. These models were chosen because they have proven their effectiveness and robustness in personnel re-identification tasks in previous studies.…”

Section: Dual-uav Stereoscopic Positioningmentioning

confidence: 99%

A Dual UAV Cooperative Positioning System With Advanced Target Detection and Localization

Chang,

Pan,

et al. 2024

IEEE Access

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Utilizing Unmanned Aerial Vehicles (UAVs) plays a pivotal role in the localization of ground construction personnel targets within the specialized operational environments of the energy industry. To ensure the safety of personnel in these areas effectively, this study introduces an innovative dual UAV cooperative positioning system designed to address the challenges of personnel localization in various complex settings. In the first place, the research establishes a dual UAV collaborative remote sensing platform, specifically tailored for executing dual UAV flight control and ground personnel identification and localization tasks. Subsequently, this study proposes a high-precision ground personnel localization solution. This solution integrates the flight data of two UAVs with the pixel coordinates of targets in natural remote sensing imagery, enabling the precise localization of moving targets within complex environments. Moreover, due to the irregularity of ground personnel shapes in imagery, this research introduces an improved rotating target detection network based on the YOLOv7 architecture. This network is specifically optimized for detecting ground targets in remote sensing imagery, significantly enhancing the accuracy of target localization. Extensive experimental validation has demonstrated that this localization system can maintain target localization errors within a 5-meter threshold, effectively enhancing the safety and efficiency of personnel in specific operational areas of the energy industry.

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“…The existing visual object segmentation and representation both are handcrafted as well as DNN-based [12]. Images with diverse set textures, shapes, and colors require a hybrid features model to include textured features, such as overlapped multi-oriented tri-scale local binary pattern (OMTLBP) [13], robustnessdriven hybrid descriptor (RDHD) [14], and discriminative local features of overlapping stripes (DLFOS) [15]. Deep neural networks developed to detect textual characters of different languages include BLPnet [16], HMM-DNN [17], distance-based edge linking (DEL) [18], and KDANet [19].…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Inspired Opinion Extraction Mechanism for Classifying Customer Reviews on Social Media

Alotaibi

2023

Applied Sciences

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Machine learning frameworks categorizing customer reviews on online products have significantly improved sales and product quality for major manufacturers. Manually scrutinizing extensive customer reviews is imprecise and time-consuming. Current product research techniques rely on text mining, neglecting audio, and image components, resulting in less productive outcomes for researchers and developers. AI-based machine learning frameworks that consider social media and online buyer reviews are essential for accurate recommendations in online e-commerce shops. This research paper proposes a novel machine-learning-based framework for categorizing customer reviews that uses a bag-of-features approach for feature extraction and a hybrid DNN framework for robust classification. We assess the performance of our machine learning framework using AliExpress and Amazon e-commerce product review data provided by customers, and we have achieved a classification accuracy of 91.5% with only 8.46% fallout. Moreover, when compared with state-of-the-art models, our proposed model shows superior performance in terms of sensitivity, specificity, precision, fallout, and accuracy.

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Person Re-Identification by Discriminative Local Features of Overlapping Stripes

Cited by 4 publications

References 43 publications

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

A Dual UAV Cooperative Positioning System With Advanced Target Detection and Localization

A Machine-Learning-Inspired Opinion Extraction Mechanism for Classifying Customer Reviews on Social Media

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