Training a Regression-Based Model for Crowd Counting in Transit Cars Using Ranked Image Pairs and Triplets

Abstract

Accurately measuring the level of crowding in transit cars is crucial for ensuring passenger safety and efficient operation. However, applying object detection algorithms to crowd counting in transit cars poses difficulties due to the low viewpoint of the cameras and the labor-intensive task of image labeling. Although some researchers have explored regression-based crowd counting methods without labeling with bounding boxes, their approaches still necessitate manual counting of passengers for image labeling. To overcome these challenges, we propose a novel calibration method for regression-based models that minimizes the number of labeled images required for training. Our approach employs image pairs and triplets with ranks for reinforcing the model training. Subsequently, the training task requires a minimal number of images labeled with exact passenger counts. Experimental results demonstrate that our proposed calibration approach considerably enhances the crowd counting performance of the conventional regression-based model. Specifically, our method reduces the mean absolute error (MAE) by 76.5% and 34.3% for conventional detection- and regression-based calibration methods, respectively.

Keywords

• Passenger load in transit
• crowd counting
• computer vision
• regression-based model
• ranking model