Low-Light Amodal Objects Tracking: A Benchmark
Bibliographic record
Abstract
Object tracking in real-world scenarios is often hampered by the simultaneous challenges of low light and partial object occlusion. While existing evaluation datasets have tackled these scenarios separately-focusing either on low-light settings or amodal perception-their co-occurrence has rarely been studied. To bridge this gap, we introduce LAOT (Low-Light Amodal Object Tracking), a benchmark tailored to assess how state-of-the-art tracking algorithms perform under the combined challenges of dim lighting and partial object occlusion. The dataset consists of 201 diverse video sequences with over 16K frames, each meticulously annotated with both modal bounding boxes (representing the visible part of the object) and amodal bounding boxes (estimating the complete object extent, including occluded regions). To characterize occlusion severity, object instances are categorized into three levels based on visible proportion: 0-20% (no occlusion), 20-80% (partial occlusion), and 80-100% (heavy occlusion). We benchmark 21 state-of-the-art tracking algorithms, including ARTrack [1], AVTrack [2], DropTrack [3], SeqTrack [4], ProContEXT [5], and HIPTrack [6], using Average Precision (AP) as the evaluation metric across occlusion levels. The results reveal a consistent decline in tracking accuracy as occlusion increases. For example, DropTrack achieves 0.8382 AP without occlusion but drops to 0.1730 AP with heavy occlusion; similarly, AVTrack falls from 0.7375 to 0.1276. These findings expose the limitations of current methods in coping with simultaneous challenges in low light and amodal perception. LAOT serves as a comprehensive and diagnostic benchmark to guide the development of robust perception-aware tracking algorithms suited for visually degraded environments.The LAOT is available athttps://github.com/LSW-CVLab/LAOT
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".