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arXiv Machine Learning

The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking

The paper investigates how detector architecture, model scale, scene density, and inference regime influence multi‑pedestrian tracking performance. It compares YOLO11, RT‑DETR, and YOLO26 detectors with ByteTrack and BoT‑SORT trackers on MOT17, MOT20, and SOMPT22 datasets, finding RT‑DETR best on HOTA but at higher cost, while YOLO26‑L with NMS balances quality and speed. End‑to‑end inference for YOLO26 did not consistently improve tracking over NMS.

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arXiv Computer Vision
Oct 2

Beyond Leaderboard Scores: A Deployment-Focused Protocol for Interpretable Tracking Evaluation in Pedestrian-Centric Environments

The paper introduces a deployment‑focused evaluation protocol for pedestrian tracking that isolates tracker performance by using shared detections, assessing initialization, gap continuation, identity recovery, close‑neighbor association, and runtime under load, while still reporting HOTA as an aggregate metric. Applied to the JackRabbot Dataset, the protocol reveals that trackers vary widely in capability, with none maintaining spatially correct, same‑identity output after 1 s of missing detector support, and that runtime on an NVIDIA Jetson Orin often falls below the 10 Hz target in crowded scenes. The authors provide open‑source code and scripts to enable reproducible assessment of tracker suitability for real‑world pedestrian‑centric robotics. whyItMatters":"The protocol offers a practical, tracker‑only framework that directly informs the design and deployment of mobile robots operating among pedestrians by highlighting specific failure modes and computational constraints that aggregate scores overlook."

By Dominik Wojcikiewicz, Diego Paez-Granados
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arXiv Computer Vision
Sep 22

Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOT

This paper conducts a systematic empirical study of multi‑object tracking (MOT) algorithms, focusing on how detection and association components affect overall performance. By evaluating state‑of‑the‑art methods on benchmarks such as MOT16/17/20, SportsMOT, DanceTrack, and CrowdTrack, the authors find that detection quality has a far greater impact than association strategies, and that transformer‑based end‑to‑end models are more robust to detection variations but computationally expensive. The study provides a unified pipeline diagram and practical guidance for researchers and practitioners in selecting and designing MOT systems.

By Linh Van Ma, Juhua Hu, Wei Cheng, Unse Fatima, Moongu Jeon
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arXiv Computer Vision
Sep 30

ByteTraX: Enhancing the ByteTrack Architecture with Optimised Thresholding

ByteTraX is a lightweight enhancement to the ByteTrack multi‑object tracking architecture that introduces a single unified matching threshold and stricter track initiation criteria to reduce erroneous track reclassification and identity switches. The method yields consistent performance gains across several benchmarks—GMOT‑40, LC‑MOT, SportsMOT, TeamTrack, DAMUNT, and DeepSea‑MOT—while boosting processing speed by over 10%. Quantitatively, ByteTraX achieves more than a 40% drop in identity switches, with mean improvements of 3.6 in HOTA, 5.6 in IDF1, and 6.3 FPS.

By Thomas A. O'Shea-Wheller
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arXiv Computer Vision
Sep 7

Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods

The paper introduces MovingDroneCrowd++, a large-scale video dataset for dense crowd counting and tracking from moving drones, featuring varied flight altitudes, camera angles, and lighting. It presents two new methods: GD3A for Video Individual Counting and GIA-Track for Multi-Object Tracking, both leveraging group-wise density assignment and identity association to handle aerial challenges. Experiments demonstrate significant improvements, reducing counting error by 47.4% and boosting tracking accuracy by 64.6%.

By Yaowu Fan, Jia Wan, Tao Han, Andy J. Ma, Wanli Ouyang, Antoni B. Chan
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arXiv Computer Vision
Sep 16

GRACE: Geometry- and Ray-Aware Camera-Efficient Multi-View Pedestrian Tracking

GRACE is a camera‑efficient multi‑view pedestrian tracker that reduces the number of required cameras while maintaining high tracking accuracy. It combines volumetric‑guided fusion of homography‑based BEV features with 3D‑lifted features, uses ray conditioning to incorporate each camera’s viewing direction, and employs BEV Track Recovery to continue existing tracks with low‑confidence detections. On the WildTrack dataset, GRACE raises MOTA from 83.54 to 91.07 compared to the baseline TrackTacular.

By Taigo Sakai, Kazuhiro Hotta, Hiroki Kouno, Naoki Kato
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