Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918088866267136 |
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| author | Jung, Seoik Song, Taekyung |
| author_facet | Jung, Seoik Song, Taekyung |
| contents | In this paper, we investigate the applicability of the CLIP-EBC framework, originally designed for crowd counting, to car object counting using the CARPK dataset. Experimental results show that our model achieves second-best performance compared to existing methods. In addition, we propose a K-means weighted clustering method to estimate object positions based on predicted density maps, indicating the framework's potential extension to localization tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08240 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework Jung, Seoik Song, Taekyung Computer Vision and Pattern Recognition I.4.8; I.2.10 In this paper, we investigate the applicability of the CLIP-EBC framework, originally designed for crowd counting, to car object counting using the CARPK dataset. Experimental results show that our model achieves second-best performance compared to existing methods. In addition, we propose a K-means weighted clustering method to estimate object positions based on predicted density maps, indicating the framework's potential extension to localization tasks. |
| title | Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework |
| topic | Computer Vision and Pattern Recognition I.4.8; I.2.10 |
| url | https://arxiv.org/abs/2507.08240 |