How would you change tracking from people to cars?

[cesarandreslopez]

First of all great job! This was most useful.

Any pointers on how to change this tracker from people to cars?

[anshu1106]

Change the part in the yolo detector code where we filter 'person' from detected boxes. In yolo.py

for i, c in reversed(list(enumerate(out_classes))): predicted_class = self.class_names[c] if predicted_class != 'person' : continue box = out_boxes[i] Change 'person' to 'car'.

[cesarandreslopez]

Excellent! Missed that, thank you for the pointer! @anshu1106

[Qidian213]

Maybe not , the deep_sort model was just trained for person

[cesarandreslopez]

I can confirm that it tracks cars, birds and trucks too and performs well.

[Qidian213]

Maybe deep_sort more like a feature coding and similarity comparator , no matter what object it is .

[Megaclick]

hi!, there's a way to track multiple objects?, like cars and trucks at the same time?

[Qidian213]

you can have a try . they all belong to "car" , just like @anshu1106 say to have a try .

[zooezou]

Thanks a lot ,btw it can be easily used to track cars. It's favoured if the relevant paper can be provide : )

[Goldwh]

I want to track other things, not people, cars, birds, etc. However, the tracking effect is not good. How should I train again?

[Qidian213]

@Goldwh You can reference to https://github.com/nwojke/deep_sort

[sherlockchou86]

yolo is used for detection, which can detect 80 classes with coco dataset. deep_sort can only calculate person appearance features with default model. even though you detect multi targets such as cars, truck, bird etc with yolo, the deep_sort tracking performace would not be well.

you need train your own model for tracking.

[lifeofzi]

hi!, there's a way to track multiple objects?, like cars and trucks at the same time?

yes.

Change the part in the yolo detector code where we filter 'person' from detected boxes. In yolo.py

for i, c in reversed(list(enumerate(out_classes))): predicted_class = self.class_names[c] if predicted_class != 'car' and predicted_class!='truck' : continue box = out_boxes[i] Change 'person' to 'car'.

[irisqp]

hi! I ran demp.py. than this error occur VIDEOIO ERROR: V4L2: Pixel format of incoming image is unsupported by OpenCV can anyone help me figure out some way to overcome this error?

[vaibhawvipul]

yolo is used for detection, which can detect 80 classes with coco dataset. deep_sort can only calculate person appearance features with default model. even though you detect multi targets such as cars, truck, bird etc with yolo, the deep_sort tracking performace would not be well.

you need train your own model for tracking.

I agree. DeepSort is trained for Person Re-Identification. I wanted to track people and cars together, I observed a lot of ID switches for vehicles. so, If you want to use the same model to track multiple objects then it would need to be trained again.