Computer Vision News - October 2021

2 Summary Coding Workshop 10 Nice to meet you everyone this month! I hope that you enjoyed the last technical coding and you didn’t have a great trouble following it. I am always eager to receive more feedback from you Let me know what computer vision tools are your favorite or if you have trouble implementing a deep learning method on visual computing and I will be happy to cover that in an article! CodingWorkshop:Creatingamulti-objecttracking model using a pre-trained RNN This month’s article is about using a pre-trained network (you can find that implementation on the GitHub MOT Challenge. MOT Challenge There is remarkable progress on object detection and association in recent years which are the core components for multi-object tracking. The main focus though has been on improving singular networks. In this example (see the original paper to read more about the topic), the proposed architecture (pre-trained network) combines two tasks in a single network to improve the inference speed. Older research has shown degraded results in this combined network, mainly because the association branch is not appropriately learned. Here, after discovering the reasons behind the failure, a simple baseline was presented to address the problems. It was shown that it remarkably outperforms the state-of-the-arts on the MOT challenge datasets at 30 FPS. Hopefully, this baseline could inspire and help evaluate new ideas in this field. Now let’s see the implementation of the pre- trained network! Creating the R-CNN With the following code you can implement a variation of the pre-trained model and use COCO to download the trained weights, import the R-CNN mask and set a specific image directory where the images can be run for the training.