A method for bee activities recognition from videos captured at the beehive entrance
Bibliographic record
Abstract
Honeybees play an important role in the ecosystem and agricultural economy. To maintain and develop healthy bee colonies, monitoring and recognizing bee activities at the beehive entrance is necessary. In this research, we extend the method in [6] to track and recognize the flight-in and flight-out activities of both pollen-bearing and non-pollen-bearing bees in videos recorded at the beehive entrance. To achieve this goal, a framework consisting of bee detection, bee tracking, and bee activity recognition is proposed. In the first step, to address the imbalance between the number of pollen-bearing and non-pollen-bearing bees, we employed a detection method combining YOLOv5 and the focal loss function. Subsequently, in the tracking step, based on the detection results of the first step, two OC-SORT-based trackers were initialized to determine the trajectories of pollen-bearing and non-pollen-bearing bees. Finally, in the activity recognition step, rules are applied to the tracked trajectories to determine the instantaneous activity states of honey bees and to recognize their overall activities. The experimental results show that the detection step obtained an overall precision of 0.972 whereas the tracking step achieved HOTA values of 77.28%, MOTA of 90.09%, and MOTP of 84.98%.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".