Code for reproducing the results in the following paper:

• High Performance Visual Tracking with Siamese Region Proposal Network
• Pytorch version has been available by my classmates.
• Another version zkisthebest/Siamese-RPN have lots of bugs.So I have to re-implementation by myself

• python=3.6
• tensorflow=1.10
• cuda=9.0

• Enter http://data.votchallenge.net/vot2013/vot2013.zip in your browser
• Unzip the file and move to ./data

• git clone https://github.com/mbuckler/youtube-bb.git
• python3 download.py ./data 12

• wget http://bvisionweb1.cs.unc.edu/ilsvrc2015/ILSVRC2015_VID.tar.gz

The red box is for tracing

bbox in detection

• red -- the groundtruth

• black -- bbox with highest score

• other colors -- bbox with scores from second to tenth.

If your data format is the same as VOT 2013, you can run the code directly. If not, you need to change the utils/image_reader.py or convert the data format to VOT format.

python train.py

If you want to see if the training is reasonable in the course of training, you can choose to turn on debug.Just change the init() in train.py

self.is_debug=True

This will result in a debug folder where you can see pictures of the training process, with groundtruth in red and box in top 10 scores in other colors.

To test series of images like VOT format

If you want to test a series of images captured from the video, you need to assign new values img_pathand img_label in config.py, which are the files of your image's path and label, respectively. Then execute the following commands

python test.py

This command will automatically synthesize videos from image sequences, and also synthesize videos from processed images, which are saved in. / data / vedio

To test a vedio

If you are testing a video, you need to put the video in./data/vedio. You can run the following command and select the object you want to track in the first frame according to the program prompt at the beginning.

python vedio_test.py test.mp4

The 'test.mp4' is the name of your vedio

I will provide the well-trained model in the next few days