How do I train the DeepSORT tracker for custom class?

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I want to detect and count the number of vines in a vineyard using Deep Learning and Computer Vision techniques. I am using the YOLOv4 object detector and training on the darknet framework. I have been able to integrate the SORT tracker into my application and it works well, but I still have the following issues:

  • The tracker sometimes reassigns a new ID to the object
  • The detector sometimes misidentifies the object (which lead to incorrect tracking)
  • The tracker sometimes does not track a detected object.

You can see an example of the reassignment issue in the following image. As you can see, in frame 40 the id 9 was a metal post, and frame 42 onwards it is being assigned to a tree

In searching for the cause of these problems, I have learnt that DeepSORT is an improved version of the SORT, which aims to handle this problem by using a Neural Network for associating tracks to detections.

Problem:

The problem I am facing is with the training of this particular model for Deepsort. I have seen that the authors have used cosine metric learning to train their model, but I am not being able to customize the learning for my custom classes. The questions I have are as follows:

  1. I have a dataset of annotated (YOLO TXT format) images which I have used to train the YOLOv4 model. Can I reuse the same dataset for the Deepsort tracker? If so, then how?

  2. If I cannot reuse the dataset, then how do I create my own dataset for training the model?

Thanks in advance for the help!

object-detection tensorflow computer-vision python
1 Answers

Yes, you can use the same classes for DeepSORT. SORT works in 2 stages, and DeepSORT adds a 3rd stage. First stage is detection, which is handled by YOLOv3, next is track association, which is handled by Kalman Filter and IOU. DeepSORT implements the 3rd stage, a Siamese network to compare the appearance features between current detections and the features of each track. I've seen implementations use ResNet as the feature embedding network

Basically once YOLO detects your class, you pass the cropped detected image over to your siamese network and it converts it into feature embeddings and compares those features with the past ones using cosine distance.

In conclusion, you can use the same YOLO classes for DeepSORT and SORT since they both need a detection stage, which is handled by YOLO.

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