Comments (3)
If you are providing new detections each time the tracker will fuse the new information and update the object states before returning a filtered bounding box. However, it sounds like you want to just give it one set of detections for the initial frame only. Unfortunately, this will not work because SORT is a tracking-by-detection method. In other words without new detections, there is no information being added to providing the same detections over and over will make the system assume stationary objects or feeding empty lists will indicate that the objects are lost.
The use case you describe is better suited for trackers which make used of appearance information, one such example could be HART.
from sort.
thanks, yes I understand.
So this tracker is tracking objects when the detections are given? meaning the output is exact objects in different frames? like a1, b1, c1 = a2, b2, c2 [the numbers are the frame num and the letters are the objects]
I will look at HART like trackers. Thanks :)
from sort.
Yes, SORT aims to match the detections across frames to form multiple object trajectories.
from sort.
Related Issues (20)
- from sort import * causes dead Kenel HOT 1
- running SORT on custom dataset
- missing intermediate IDs HOT 2
- Using SORT when the number of elements to track is constant and known - ideas to recover missidentification and missmatches HOT 6
- C-Python implementation of SORT
- Multi-class multi-object tracking HOT 3
- error in iou__batches HOT 7
- Tracking other objects HOT 2
- Why are FP and FN different for each Tracker method in the paper?
- Preparing RLE for MOTS20 evaluation
- SORT ID for new detection between other old detections
- Logic Error? trk.hit_streak >=self.min_hits, ret.append(...)
- multiclass tracking HOT 3
- can I use this for windows ? because it still show me the same error ?
- No issue, just here for `lap` -> `lapx`
- instalation error
- How to get next state KalmanFilter
- ValueError: operands could not be broadcast together with shapes (0,) (1,3) HOT 1
- Python Version support for SORT
- Converting into Grayscale increasing the inference time ? Is there any way we can increase the model inference time ? HOT 1
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from sort.