Have a personal or library account? Click to login
Backpack detection model using multi-scale superpixel and body-part segmentation Cover

Backpack detection model using multi-scale superpixel and body-part segmentation

International Journal on Smart Sensing and Intelligent Systems
Volume 16 (2023): Issue 1 (January 2023)
By: Rahmad Hidayat,  Agus Harjoko and  Aina Musdholifah  
Open Access
|Aug 2023

Figures & Tables

Figure 1:

Backpack detection using multi-scale superpixel segmentation and body-part method.
Backpack detection using multi-scale superpixel segmentation and body-part method.

Figure 2:

The resulting image of the foreground detection process on each dataset.
The resulting image of the foreground detection process on each dataset.

Figure 3:

Cell and block in 64 × 128 image.
Cell and block in 64 × 128 image.

Figure 4:

Human Body Proportion Model [29].
Human Body Proportion Model [29].

Figure 5:

Heads segment sample.
Heads segment sample.

Figure 6:

Bend-line identification and superpixel selection process.
Bend-line identification and superpixel selection process.

Figure 7:

Camera configuration in the acquisition room.
Camera configuration in the acquisition room.

Figure 8:

The segmentation results on the l1, l2, and l3 scales.
The segmentation results on the l1, l2, and l3 scales.

Figure 9:

The result of bend-line determining process on image with scale l2.
The result of bend-line determining process on image with scale l2.

Figure 10:

Example of Features Extraction on Selected Superpixels (B+h).
Example of Features Extraction on Selected Superpixels (B+h).

Figure 11:

The ROC curve for each scenario on the DIKE20 dataset.
The ROC curve for each scenario on the DIKE20 dataset.

Figure 12:

The ROC curve for each scenario on the PETS2006 dataset.
The ROC curve for each scenario on the PETS2006 dataset.

Figure 13:

The ROC curve for each scenario on the i-LIDS dataset.
The ROC curve for each scenario on the i-LIDS dataset.

The selected superpixels and their location based on bend line

SuperpixelsLocation
B + 3h
B + 2h
B + 2h
B + h
B + h

Comparison of precision, recall, and F1 scores on the PETS2006 dataset

MethodsPrecisionRecallF1 score
Damen and Hog (2012)50%55%52%
Ghadiri et al. (2017)57%71%63%
Ghadiri et al. (2019)60%79%68%
Proposed Methods
BP_SC156%85%68%
BP_SC259%83%69%

SLIC segmentation

1:Centroid Initialization Ck=[lk,ak,bk,xk,yk]T
2:Put centroid in n × n window
3:  repeat
4:for each cluster Ck do
5:Group each pixel in the nearest centroid (based on measurement of pixel distance to centroid)
    end for
6:Update centroid
7:until centroid unchanged

Number of test images in each dataset

DatasetTest Images
DIKE20271
PETS2006323
i-LIDS185
Total779

The precision, recall, and F1 scores on DIKE20 dataset

MethodsPrecisionRecallF1 score
BP_SC146%79%60%
BP_SC252%80%63%

Comparison of precision, recall, and F1 scores on the i-LIDS dataset

MethodsPrecisionRecallF1 score
Damen and Hog (2012)52%47%49%
Ghadiri et al. (2017)62%60%61%
Ghadiri et al. (2019)72%64%67%
Proposed Methods
BP_SC149%90%64%
BP_SC252%95%67%
DOI: https://doi.org/10.2478/ijssis-2023-0008 | Journal eISSN: 1178-5608
Journal RSS Feed
Language: English
Submitted on: Oct 9, 2021
Published on: Aug 19, 2023
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
backpack,
superpixels,
body-part,
bend line,
HOG,
histogram
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2023 Rahmad Hidayat, Agus Harjoko, Aina Musdholifah, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Previous articleVolume 16 (2023): Issue 1 (January 2023)Next article