Summary
I would like to suggest an experimental sampling approach that may be worth investigating for KCF-style tracking.
The idea came about during some data processing optimisation experiments I was running. Object tracking seemed like a good candidate, and this pattern popped into my head.
The idea is to replace the uniformly sampled rectangular search region with a hexagonal foveated sampling pattern, where sampling cells become progressively smaller toward the current target position and progressively larger toward the search-region boundary.
The intention is to concentrate spatial resolution where accurate localisation is most valuable while reducing sampling density farther from the predicted target centre.
Pattern
The important distinction is that this is not simply a coarse hexagonal grid.
The cell size changes smoothly with distance from the centre, producing a continuously warped sampling field:
- small, dense hexagonal cells near the tracked target;
- progressively larger cells with increasing radius;
- no abrupt boundary between high- and low-resolution regions.
Conceptually:
regular:
uniform spatial sampling everywhere
coarse hex:
uniformly reduced sampling density
smooth warped hex:
high density near target
progressively lower density toward periphery
Exploratory test
I ran a small exploratory comparison of:
- Regular KCF sampling
- Uniformly coarse hexagonal sampling
- Smoothly warped hexagonal foveated sampling
The exploratory results were:
| Sampling method |
Mean IoU |
Frames with IoU >= 0.5 |
Approx. sampling/work reduction |
| Regular |
0.314 |
29.8% |
baseline |
| Coarse hexagonal |
0.300 |
30.0% |
~75% |
| Smooth warped hexagonal |
0.562 |
67.3% |
~75% |
The coarse representation therefore appeared to retain approximately the same tracking performance while using substantially fewer samples.
More interestingly, the smoothly warped representation produced substantially higher localisation accuracy at approximately the same reduced sampling resolution.
Possible explanation
One possibility is that the smoothly warped representation gives KCF a useful compromise:
- very accurate correlation/localisation near the predicted target position;
- sufficient lower-resolution peripheral information to detect displacement;
- substantially fewer uniformly high-resolution samples.
This could be particularly appropriate for tracking because the target is normally expected to remain reasonably close to the previous predicted location.
Important caveats
These results should be treated only as an exploratory performance suggestion, not as a benchmark result.
The test did not use the official OpenCV KCF implementation.
It used a small self-contained grayscale Gaussian-kernel KCF implementation and controlled generated tracking sequences.
The experiment also did not use established datasets such as OTB, VOT, LaSOT, TrackingNet, etc.
The reported workload reduction is primarily based on the reduced sampling / correlation-grid size rather than a fully optimized production implementation.
Therefore I am not suggesting that the numbers above generalize directly to the production tracker.
The interesting result is simply that:
uniform coarse sampling retained roughly baseline behaviour, while smoothly increasing spatial resolution toward the target centre produced a sizeable improvement in this exploratory test.
Suggested experiment
It may be worth testing this independently using the standard KCF implementation and established tracking benchmarks:
regular KCF
vs
fixed coarse hexagonal sampling
vs
smoothly warped hexagonal foveated sampling
Useful measurements would include:
- IoU / success rate
- precision
- target-loss rate
- reacquisition behaviour
- FPS / latency
- amount of image/search-region data processed
- CPU/GPU workload
It would also be useful to test separately under:
- slow motion
- rapid motion
- scale changes
- occlusion
- distractors
- camera motion
This is only being raised as a possible optimisation direction for maintainers to investigate. Testing was carried out by machine-guided sandbox experiments - the concept is mine, but it does no good sitting here in my head, so I thought I'd put it out here.
Summary
I would like to suggest an experimental sampling approach that may be worth investigating for KCF-style tracking.
The idea came about during some data processing optimisation experiments I was running. Object tracking seemed like a good candidate, and this pattern popped into my head.
The idea is to replace the uniformly sampled rectangular search region with a hexagonal foveated sampling pattern, where sampling cells become progressively smaller toward the current target position and progressively larger toward the search-region boundary.
The intention is to concentrate spatial resolution where accurate localisation is most valuable while reducing sampling density farther from the predicted target centre.
Pattern
The important distinction is that this is not simply a coarse hexagonal grid.
The cell size changes smoothly with distance from the centre, producing a continuously warped sampling field:
Conceptually:
regular:
uniform spatial sampling everywhere
coarse hex:
uniformly reduced sampling density
smooth warped hex:
high density near target
progressively lower density toward periphery
Exploratory test
I ran a small exploratory comparison of:
The exploratory results were:
The coarse representation therefore appeared to retain approximately the same tracking performance while using substantially fewer samples.
More interestingly, the smoothly warped representation produced substantially higher localisation accuracy at approximately the same reduced sampling resolution.
Possible explanation
One possibility is that the smoothly warped representation gives KCF a useful compromise:
This could be particularly appropriate for tracking because the target is normally expected to remain reasonably close to the previous predicted location.
Important caveats
These results should be treated only as an exploratory performance suggestion, not as a benchmark result.
The test did not use the official OpenCV KCF implementation.
It used a small self-contained grayscale Gaussian-kernel KCF implementation and controlled generated tracking sequences.
The experiment also did not use established datasets such as OTB, VOT, LaSOT, TrackingNet, etc.
The reported workload reduction is primarily based on the reduced sampling / correlation-grid size rather than a fully optimized production implementation.
Therefore I am not suggesting that the numbers above generalize directly to the production tracker.
The interesting result is simply that:
Suggested experiment
It may be worth testing this independently using the standard KCF implementation and established tracking benchmarks:
Useful measurements would include:
It would also be useful to test separately under:
This is only being raised as a possible optimisation direction for maintainers to investigate. Testing was carried out by machine-guided sandbox experiments - the concept is mine, but it does no good sitting here in my head, so I thought I'd put it out here.