# Multi-Person tracking by multi-scale detection in Basketball scenarios

## TL;DR

This paper introduces a multi-scale detection approach for multi-person tracking in basketball videos, addressing occlusion challenges and enabling advanced statistical analysis for performance enhancement.

## Contribution

It presents a novel multi-scale detection method and a new dataset for improved multi-person tracking in basketball scenarios.

## Key findings

- Achieved high detection accuracy with F1-score
- Attained notable tracking performance with MOTA
- Demonstrated system's potential for statistical analysis

## Abstract

Tracking data is a powerful tool for basketball teams in order to extract advanced semantic information and statistics that might lead to a performance boost. However, multi-person tracking is a challenging task to solve in single-camera video sequences, given the frequent occlusions and cluttering that occur in a restricted scenario. In this paper, a novel multi-scale detection method is presented, which is later used to extract geometric and content features, resulting in a multi-person video tracking system. Having built a dataset from scratch together with its ground truth (more than 10k bounding boxes), standard metrics are evaluated, obtaining notable results both in terms of detection (F1-score) and tracking (MOTA). The presented system could be used as a source of data gathering in order to extract useful statistics and semantic analyses a posteriori.