Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework

Abstract

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.

4 pages, 2 figures, submitted to a computer vision conference