Advancing Multi-Person Tracking for Autism Behavior Analysis: Challenges, Opportunities, and Future Directions in Clinical Settings

Multi-person tracking (MPT) has emerged as a crucial tool for behavior monitoring in clinical settings, offering significant potential for advancing autism research. In this paper, we assess the state of the art in MPT, with a specific focus on its application to autism spectrum disorder (ASD) behavior analysis. Clinical environments present unique challenges for MPT, including frequent occlusions, visually similar appearances such as uniforms, and complex social interactions. We explore these challenges in depth while highlighting recent advancements in tracking algorithms that present opportunities for improving behavioral monitoring in healthcare. Through a comprehensive review, we examine the limitations of existing MPT methods when applied to our clinical dataset, BAV-ASD (behavioral analysis videoset for ASD), discussing their applicability and gaps in addressing the unique demands of clinical autism studies. Our findings emphasize the critical role of MPT in enabling non-intrusive, accurate, and continuous monitoring of challenging behaviors, such as aggression and self-injury, in individuals with ASD. By addressing the limitations of existing methods, this work paves the way for future research aimed at enhancing the reliability and effectiveness of MPT systems in clinical environments, ultimately supporting early intervention strategies and improving out-comes for individuals with ASD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>Our code can be found at https://github.com/ostadabbas/ASD-Tracking-Challenges. Supported by NSF-CAREER Grant #2143882 and #R01LM014191..