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Showing 1 - 20 results of 586 for search '(( data tracking algorithm ) OR ((( develop box algorithm ) OR ( element update algorithm ))))', query time: 0.43s Refine Results
  1. 1
    Assignment of Hungarian Algorithm.

    Assignment of Hungarian Algorithm. by Yibin Zhang (1426579)

    Published 2025
    “…In this paper, a new tracking mechanism is proposed for real-time tracking, which is based on the 2D LiDAR data structure with the Simple Online and Real-Time Tracking (SORT) algorithm. …”
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  2. 2
    Diagram illustrating how the iterative bounding box refinement method works with cell divisions. When tracking the cell from t<sub>0</sub> to t<sub>1</sub>, the bounding box from t<sub>0</sub> serves as the initial bounding box that is iteratively refined three times into a bounding box that locates the cell in t<sub>1</sub>. The second bounding box is ignored here since cell division is not predicted at time t<sub>1</sub>. This method consists of predicting a relative offset with respect to the previous bounding box, represented by a chang

    Diagram illustrating how the iterative bounding box refinement method works with cell divisions. When tracking the cell from t<sub>0</sub> to t<sub>1</sub>, the bounding box from t... by Owen M. O’Connor (11965460)

    Published 2025
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  3. 3
    Cell-TRACTR detects cells in the first frame at time t 0. Object queries predicted to be a “cell” are converted to track queries. For Track Group Denoising (TGD), random noise is added to the track queries to get noised track queries. For Query Denoising (QD), random noise is added to the ground truth bounding boxes to generate noised track queries. All queries are processed together by the decoder. Attention masks are used to prevent information leakage between the object and track queries, the noised track queries generated from TGD, and the noised track queries generated from QD. The gray boxes indicate information being blocked between sets of queries while colored boxes indicate the flow of information.

    Cell-TRACTR detects cells in the first frame at time t 0. Object queries predicted to be a “cell” are converted to track queries. For Track Group Denoising (TGD), random noise is a... by Owen M. O’Connor (11965460)

    Published 2025
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  4. 4
    Depiction of how predictions are made from the output embeddings. The largest encoded multi-scale feature map is resized and combined with the original multi-scale features to form the pixel embedding map. The dot product of the output embeddings and the pixel embedding map produces the segmentation masks. A linear layer is used to predict the class label and a multi-layer perceptron (MLP) is used to generate the bounding box. For output embeddings classified as “no object”, the bounding boxes and segmentation masks are ignored.

    Depiction of how predictions are made from the output embeddings. The largest encoded multi-scale feature map is resized and combined with the original multi-scale features to form... by Owen M. O’Connor (11965460)

    Published 2025
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  5. 5
    Novel S-Box rendered by the suggested algorithm.

    Novel S-Box rendered by the suggested algorithm. by Nayeem Ahmad Khan (11727377)

    Published 2025
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  6. 6
    Overall framework of our proposed tracking algorithm.

    Overall framework of our proposed tracking algorithm. by Yi Tao (178829)

    Published 2025
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  7. 7
    Box plots of different algorithms.

    Box plots of different algorithms. by Kuiyuan Pan (21145338)

    Published 2025
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  8. 8
    Excerpts from the raw acquired data set with the tracking algorithm.

    Excerpts from the raw acquired data set with the tracking algorithm. by Hannes Benecke (17409369)

    Published 2025
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  9. 9
    Visualization of tracking results of SiamFC_TDA algorithm and comparison algorithm on challenging sequences.

    Visualization of tracking results of SiamFC_TDA algorithm and comparison algorithm on challenging sequences. by Yi Tao (178829)

    Published 2025
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  10. 10
    Performance of the proposed tracking algorithm and comparison algorithms in three datasets. The higher the metrics, the better.

    Performance of the proposed tracking algorithm and comparison algorithms in three datasets. The higher the metrics, the better. by Yi Tao (178829)

    Published 2025
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  11. 11
    Results of different algorithms for ship detection and tracking.

    Results of different algorithms for ship detection and tracking. by Guoqing Zhang (151441)

    Published 2025
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  12. 12
    Schematic demonstrating how Cell-HOTA handles early and late divisions. For early divisions, the ground truth cell is originally matched with one of the tracker cells at time t<sub>1</sub>. The red line indicates the matched cells, and the dotted blue box shows how the IOU is computed. To account for an early division, the IOU is computed between the ground truth cell in t<sub>1</sub> and both tracker cells in t<sub>1</sub>. In addition, the temporal association for the ground truth cell in t<sub>1</sub> is altered to match the tracker cell in t

    Schematic demonstrating how Cell-HOTA handles early and late divisions. For early divisions, the ground truth cell is originally matched with one of the tracker cells at time t<sub... by Owen M. O’Connor (11965460)

    Published 2025
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  13. 13
    The encoder employs deformable self-attention to transform multi-scale features into encoded multi-scale features. Query selection is used to derive the object queries from these encoded multi-scale features. Each encoded image feature undergoes classification, and the top-K features are selected as the content embeddings for the object queries. Additionally, bounding boxes are extracted from the top-K segmentation masks and are subsequently converted into positional embeddings. Following this, the object queries perform self-attention and deformable cross-attention with the encoded multi-scale features to produce the output embeddings. MLP, multi-layer perceptron.

    The encoder employs deformable self-attention to transform multi-scale features into encoded multi-scale features. Query selection is used to derive the object queries from these e... by Owen M. O’Connor (11965460)

    Published 2025
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  14. 14
    Plot comparing the circular tracking errors of the three algorithms.

    Plot comparing the circular tracking errors of the three algorithms. by Xiaosong Liu (66661)

    Published 2025
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  15. 15
    Raw data at Lubbock Intersection at Frame 53.

    Raw data at Lubbock Intersection at Frame 53. by Yibin Zhang (1426579)

    Published 2025
    “…In this paper, a new tracking mechanism is proposed for real-time tracking, which is based on the 2D LiDAR data structure with the Simple Online and Real-Time Tracking (SORT) algorithm. …”
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  16. 16
    Plot comparing the circular angle tracking performances of the three algorithms.

    Plot comparing the circular angle tracking performances of the three algorithms. by Xiaosong Liu (66661)

    Published 2025
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  17. 17
    Statistics of DbLV and z Coordinate of Raw Data.

    Statistics of DbLV and z Coordinate of Raw Data. by Yibin Zhang (1426579)

    Published 2025
    “…In this paper, a new tracking mechanism is proposed for real-time tracking, which is based on the 2D LiDAR data structure with the Simple Online and Real-Time Tracking (SORT) algorithm. …”
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  18. 18
    Shifting of integers from 1D s-box to 3D scrambled s-box’.

    Shifting of integers from 1D s-box to 3D scrambled s-box’. by Nayeem Ahmad Khan (11727377)

    Published 2025
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  19. 19
    Initial state of a S-Box.

    Initial state of a S-Box. by Nayeem Ahmad Khan (11727377)

    Published 2025
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  20. 20
    Final state of a S-Box.

    Final state of a S-Box. by Nayeem Ahmad Khan (11727377)

    Published 2025
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