Ren et al 2023a - Revision history

99003113 at 14:22, 6 June 2025

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99003113 at 14:20, 6 June 2025

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Rimni at 15:31, 29 November 2023

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Rimni at 15:29, 29 November 2023

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Rimni at 15:29, 29 November 2023

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Rimni: /* References */

2023-11-29T15:27:47Z

‎References

Rimni: /* References */

2023-11-29T15:25:32Z

‎References

Rimni at 15:18, 29 November 2023

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Rimni at 15:36, 27 November 2023

2023-11-27T15:36:42Z

Rimni at 15:10, 27 November 2023

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 , Guangdong Atv Academy For Performing Arts, Guangdong, China
 , Department of Computer Science, Baoji University of Arts and Sciences, Baoji, China
 , Software Convergence Engineering Department, Kunsan National University, Gunsan, 54150, Korea;
 , Department of Mechanical Engineering, Kunsan National University, Gunsan, 54150,  Korea
 '''*''' Correspondence: renchengjuan163[mailto:@163.com; @163.com;]
 -->

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 The performance of the proposed detection model is proven on MOT16 and KITTI datasets. The KITTI dataset is a dataset for autonomous driving, with cars and pedestrians. The MOT16 dataset is proposed in 2016 to measure standards for multi-object tracking detection and tracking methods. The performance of the proposed multi-object tracking algorithm is evaluated on the public MOT16 dataset [32]. The MOT16 benchmark evaluates the tracking performance of challenging test sequences, including forward-looking scenes with moving cameras and top-down surveillance settings. The dataset was divided into a training set and validation set. Finally, it is compared with other multi-object tracking algorithms on the test set of MOT16. The evaluation metrics are:
-MOTA: Multi-Object Tracking Accuracy:
+''MOTA: Multi-Object Tracking Accuracy'':
 {| class="formulaSCP" style="width: 77%;border-collapse: collapse;width: 100%;text-align: center;"
@@ Line 369: / Line 369: @@
 GT is the number of Groundtruth. FN stands for the missing number. The number of frames <math> t </math> in which the object has no hypothetical position to match. FP is the number of object mismatches. The number of frames <math> t </math> in which the given hypothetical position has no tracking object to match it. IDs is a mismatch. The number of times the tracking object has an ID switch in frame <math> t </math>.
-MOTP: Multi-object tracking accuracy:
+''MOTP: Multi-object tracking accuracy'':
 {| class="formulaSCP" style="width: 77%;border-collapse: collapse;width: 100%;text-align: center;"

@@ Line 97: / Line 97: @@
 ''Step 2'': Update the tracked Bbox after the matching is completed.
-''Step 3'': After updating, the current frame is predicted, the next frame is observed, and updated; then predicted again. The next frame is observed and updated etc.
+''Step 3'': After updating, the current frame is predicted, the next frame is observed, and updated; then predicted again. The next frame is observed and updated, etc.
 ===3.1.  Improved YOLOv3 network===

@@ Line 83: / Line 83: @@
 '''Detection phase''':
-''Step 1'': Split the original image into S*S cells or grids; each cell produces K bounding boxes according to the number of anchor boxes
+''Step 1'': Split the original image into S*S cells or grids; each cell produces K bounding boxes according to the number of anchor boxes.
-''Step 2'': Employ the convolutional neural network to get features and predict the <math>b=[b_x, b_y, b_h, b_c]^T</math>, and the <math>class=[C_1,C_2,C_3,\ldots ,C_c]^T</math>
+''Step 2'': Employ the convolutional neural network to get features and predict the <math>b=[b_x, b_y, b_h, b_c]^T</math>, and the <math>class=[C_1,C_2,C_3,\ldots ,C_c]^T</math>.
 ''Step 3'': Compare the maximum confidence  <math display="inline">{IoU}_{thres}^{truth}</math> of the <math>K</math> bounding boxes with the threshold <math display="inline">{IoU}_{thres}</math>, if <math display="inline">{IoU}_{thres}^{truth} > IoU_{thres}</math>, the bounding box has the object. Else, the bounding boxes not contain the object.

@@ Line 781: / Line 781: @@
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