Simple Online Realtime Tracking

A simple algorithm for multi-objects tracking.

1. Detector

The detector can be any model that can detect objects you want to track , such as person, car or animal. Outputs of detector should be: [x, y, a, h]. x, y - the center of bounding box; a - aspect ratio(w / h);

if you set ratio=h/w, the maha distance will be larger. And this will cause the maha distance become bigger.

So, in sort algorithm, author set aspect to be constant.

And, in my experiment i don't consider a and h in computing gating distance.

h - height of bounding box.

2. Tracking Method

2.1 Estimation Model

1. The first frame should be initialized:

   measurement: [x, y, a, h] --> mean: [x, y, a, h, dx, dy, da, dy] & covariance (8, 8).

2. The initial of mean is [x, y, a, h, 0, 0, 0, 0].

3. Propagate current frame state into the next frame by using a linear constant velocity model.

   If a target is associated to a detection(measurement), then update the target with kalman filter.

   If no detection is associated to the target, just predict the target with linear velocity model without correcting.

2.2 Data Association

1. Predicet next frame state from current frame state through linear velocity model.

2. The data distribution will be changed through transforming, and we call this prediction. $$ mean'=Fk*mean $$

   $$ cov'=FkcovFk^T $$

3. Compute maha distance between prediction of this frame and detection of this frame.

4. Assign matched detection to target that we track.

5. Update prediction if it has been matched with kalman gain.

6. Use iou matching to match unconfirmed trackers of age = 1 to remain unmatched detections.

2.3 Create and Delete Trackers

1. Delete trackers of age > 30 or tentative trackers of age > 3.

2. Create trackers with unmatched detections at this frame.

3. Deep Sort

There is a problem in SORT:

if motion uncertainty is slow, maha distance is a comfortable metric.

But, if motion uncertainty is big, maha distance is not stable.

So, DeepSort add a performance descriptor to promote tracking stability.

4. How to Use

1. Set your own test_video_path, model_path and target.

   test_video_path = 'rtsp://admin:[email protected]/Streaming/Channels/1'  # set your own video path
   model_path = './saved_model_coco'  # set your own model path

2. Modify video_pred.py.

   # modify code and set your own target
   # track_target: 0-person; 1-bicycle; 2-car; 7-truck
   main(test_video_path, model_path, track_target=0, visualize=True)

3. Results.

   

5. ToDO

• DeepSort