Understanding Identity Continuity in Thermal Video through Scene-Level Consistency
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917553552490496 |
|---|---|
| author | Sun, Wei-Chieh Ko, Gyungmin Kwon, Heejae Huang, Hsiang-Wei Hwang, Jenq-Neng |
| author_facet | Sun, Wei-Chieh Ko, Gyungmin Kwon, Heejae Huang, Hsiang-Wei Hwang, Jenq-Neng |
| contents | Thermal pedestrian MOT remains challenging because weak appearance cues and frequent detection interruptions cause severe trajectory fragmentation. We study whether lightweight post-processing can recover identity continuity without relying on heavy re-identification models or complex online association. Starting from a YOLOv8 and SORT baseline, we add a modular identity-repair backend consisting of online short-gap remapping and offline tracklet relinking based on temporal, spatial, motion, and border cues. Controlled ablations on a fixed validation split and evaluation on the official PBVS Thermal Pedestrian MOT benchmark show that the main identity gains arise from conservative relinking, improving IDF1 from 82.25 to 84.93 while preserving MOTA, whereas many heuristic thresholds remain stable across broad operating ranges. These results suggest that, in low-information thermal imagery, robust identity recovery can be achieved more effectively through high-precision trajectory relinking than through increasing tracker complexity. These results provide a controlled analysis of identity recovery in thermal video, showing that scene-level spatial-temporal consistency plays a dominant role in identity continuity compared to local frame-to-frame association. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01694 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Understanding Identity Continuity in Thermal Video through Scene-Level Consistency Sun, Wei-Chieh Ko, Gyungmin Kwon, Heejae Huang, Hsiang-Wei Hwang, Jenq-Neng Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multimedia I.4.8; I.4.9 Thermal pedestrian MOT remains challenging because weak appearance cues and frequent detection interruptions cause severe trajectory fragmentation. We study whether lightweight post-processing can recover identity continuity without relying on heavy re-identification models or complex online association. Starting from a YOLOv8 and SORT baseline, we add a modular identity-repair backend consisting of online short-gap remapping and offline tracklet relinking based on temporal, spatial, motion, and border cues. Controlled ablations on a fixed validation split and evaluation on the official PBVS Thermal Pedestrian MOT benchmark show that the main identity gains arise from conservative relinking, improving IDF1 from 82.25 to 84.93 while preserving MOTA, whereas many heuristic thresholds remain stable across broad operating ranges. These results suggest that, in low-information thermal imagery, robust identity recovery can be achieved more effectively through high-precision trajectory relinking than through increasing tracker complexity. These results provide a controlled analysis of identity recovery in thermal video, showing that scene-level spatial-temporal consistency plays a dominant role in identity continuity compared to local frame-to-frame association. |
| title | Understanding Identity Continuity in Thermal Video through Scene-Level Consistency |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multimedia I.4.8; I.4.9 |
| url | https://arxiv.org/abs/2606.01694 |