Occlusion Handling in Object Detection
Bibliographic record
Abstract
Object tracking is a process that follows an object through consecutive frames of images to determine the object’s movement relative other objects of those frames. In other words, tracking is the problem of estimating the trajectory of an object in the image plane as it moves around a scene. This chapter presents research that deals with the problem of tracking objects when they are occluded. An object can be partially or fully occluded. Depending on the tracking domain, a tracker can deal with partial and full object occlusions using features such as colour and texture. But sometimes it fails to detect the objects after occlusion. The shape feature of an individual object can provide additional information while combined with colour and texture features. It has been observed that with the same colour and texture if two object’s shape information is taken then these two objects can be detected after the occlusion has occurred. From this observation, a new and a very simple algorithm is presented in this chapter, which is able to track objects after occlusion even if the colour and textures are the same. Some experimental results are shown along with several case studies to compare the effectiveness of the shape features against colour and texture features.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".