Contine reading papers about long term tracking.
- Robust Long-Term Object Tracking via Improved Discriminative Model Prediction
The paper try to modify the superdimp to a long-term tracker. It present a global search method and
(1) Baseline tracker using random erasing.
Method: Erase a random small rectangular areas of image to confirm whether the prediction is reliable.
Evaluation：I hope it works.
(2) Global search using random searching.
Method: First, we create global searching templates with a predetermined interval. Next, we adaptively determine the number of searches according to the ratio of the image size to the target size. Then, an object is detected within a randomly selected searching area.
(3) Score penalty.
However, the probability of an object disappearing and suddenly appearing at a distant location is very low. To prevent this sudden detection, we penalize a confidence score through spatio-temporal constraints, which is expressed as follows:
