郑栩宁 🤔
郑栩宁 Xuning Zheng

Undergraduate

Southeast University

About Me

Studied under Prof. Siyu Xia, I’m a third year undergraduate student from School of automation, Southeast University. My research interest includes Adversarial Machine Learning, Computer Vision, Pattern Recognition, and so on.

By the way, my Email is zhengxuning@seu.edu.cn

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Interests
  • Adversarial Machine Learning
  • Computer Vision
  • Pattern Recognition
Education

  • Robotics

    Southeast University

📚 My Research

The current research direction is black-box attacks on face recognition systems by using adversarial neural networks to generate face images with interference (watermarks), thus interfering with the recognition results of the face recognition system. Innovative black box attack methods are also one of my research directions.🤔

Meanwhile, I am also interested in the directions in the Interests section, and hope to participate in them as well if I have the opportunity.😋

Please contact me to collaborate 😃

Projects
Tennis Court Registration
Tennis Court Registration

Tennis Court Registration

Tennis Registration v3 Prepare the environment DeepSORT environment cd YOLOv9_DeepSORT pip install -r requirements.txt YOLOv9 environment cd yolov9 pip install -r requirements.txt UI environment (cpp-OpenCV & sysv_ipc) cpp-OpenCV v4.7.0 in Ubuntu (Here is the tutorial) pip install sysv_ipc Demo You can use the code below to simply run the demo. python tennis_registration.py After running all frames, you will see the result in the terminal. This is the position of every player in the image coordinate. [[ 31.76 8.2214] [ 22.868 10.646] [ 11.693 7.4278] [ 6.3028 9.9999]] Or if you want to see the result, you can run the ui at the same time. cd ui ./shr_tennis_ui Then you will see like the image below. Usage You can use your own params to run the program with the code below. python tennis_registration.py --video <str> --conf <float 0~1> --blur_id <int> --class_id <int> --cam_id <int> Params If you just simply test its performance in different perspectives, just change its video and cam_id params. Params Function Default video Path to input video or video stream (input ‘0’) ‘./data/video_3.mp4’ conf confidence threshold 0.50 blur_id class ID to apply Gaussian Blur None class_id class ID to track 0 cam_id camera perspective ID to track 3 Supplements ① Stream Mode If you need to use Stream Mode, pls apply this two code in tennis_registration.py after you connect the camera. Then input ‘0’ to video param. And don’t forget to change a correct camera index to calculate homography matrix. (You can rewrite cam_dict in object_tracking_stream to fix the correspondence between cameras and indexs) # from YOLOv9_DeepSORT.yolov9.object_tracking_stream import person_track_stream # person_track_stream(FLAGS) Because I cannot connect my computer with cameras in the stadium, I commented them out while debugging the code. (If there are bugs when using Stream Mode, pls contact me…) ② Differences between object_tracking_stream and object_tracking The code of video stream is in object_tracking_stream.py, and the code of video file is in object_tracking.py They use different methods to get frames: class PYLON_SHM and VideoCapture function in OpenCV. ③ Video samples and cam_id The indexs of videos in the data folder are corresponding to the indexs of cameras. video: video_0.mp4 -> cam_id: 0 video: video_1.mp4 -> cam_id: 1 video: video_2.mp4 -> cam_id: 2 video: video_3.mp4 -> cam_id: 3 References YOLOv9_DeepSORT Learn Homography: 相机标定系列（二）单应矩阵 Homography Applcation: OpenCV-Python单应矩阵（Homography Matrix）应用——更换广告牌

zxn091651.github.io
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Publications
FAIC-Attack: An Adversarial Watermarking Attack against Face Age based on Identity Constraint
Recent Talks
ICMVA 2024
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