ArticleSensors (Basel, Switzerland)2026
People Counting Using YOLO-Based Detection and Clustering for a Mobile Robot.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- People Counting Using YOLO-Based Detection and Clustering for a Mobile Robot.Sensors (Basel, Switzerland) · 2026Article
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Authors and funding
3 authors.
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Abstract
People counting is one of the key tasks in intelligent monitoring systems. However, accurately counting people in dynamic environments can be extremely challenging. This is especially true in mobile robot applications, where challenges such as a moving camera, varying conditions, and a limited field of view due to environmental obstacles arise. In such scenarios, the people counting task primarily involves visually detecting and grouping individuals to determine the total number of unique people. This paper presents a people counting algorithm based on visual people detection and clustering. The method utilizes the You Only Look Once (YOLO) detector to identify the bounding boxes of detected individuals and extract features from their corresponding regions of interest (ROIs). Additionally, the dimension of the extracted features is reduced using an encoder and clustered to distinguish individuals, with the number of clusters serving as an estimate of the number of people. The method was tested on a dataset containing 45 independent sequences with a total of 19,350 RGB images, complete with metadata for people detection and re-identification. This dataset encompasses various settings, particularly scenarios featuring mobile robots moving and capturing frames in indoor environments. The experiments demonstrate the effectiveness of the proposed method in different configurations. The best results were achieved using the YOLOv10n detector combined with K-means or SK-means clustering, yielding a Mean Absolute Error (MAE) of 1.11. The proposed encoder-based method significantly reduces clustering time and operates effectively within the limited resources available on mobile robotic platforms such as the Jetson Nano.
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