Team members: 이재윤(Jaeyoon Lee), 정은서(Eunseo Jeong), 여찬영(Chanyeong Yeo)
In our project, Fair MOT and Linux 802.11n CSI tool library were used. Each library was used when recognizing and tracking objects from the video data received from the initial camera and when collecting and quantifying Wi-Fi CSI data from the AP.
- We use the 802.11n CSI Tool for Wi-Fi Sensing. So Reference this link and install 802.11n CSI Tool
- Install Matlab program, and Downlad Our CSI file(Reference for Realtime-processing-for-csitool, linux-80211n-csitool-supplementary) this file read csi data and output csi matrix
- Install success next step
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our File:
git clone https://github.com/jyoonlee/GCU_WifiSensing.git
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In matlab:
cd CSI cd matlab run read_bf_socket using Matlab
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In terminal:
sudo stop network-manager sudo modprobe -r iwlwifi mac80211 sudo modprobe iwlwifi connector_log=0x1
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In other terminal(connect Wifi):
iw dev sudo ip link show wlan0 sudo ip link set wlan0 up iw wlan0 link sudo iw dev wlan0 connect [WiFi name] iw wlan0 link sudo dhclient wlan0
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connet csi:
cd CSI cd linux-80211n-csitool-supplementary-master/netlink gcc log_to_server.c -o log_to_server sudo ./log_to_server 127.0.0.1 8090
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ping test
ping -i 0.2 192.168.1.1
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We used FairMOT model and changed the code according to us.
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We referred to this link for FairMOT code.
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installation
conda create -n FairMOT conda activate FairMOT conda install pytorch==1.7.0 torchvision==0.8.0 cudatoolkit=10.2 -c pytorch cd ${FAIRMOT_ROOT} pip install -r requirements.txt
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baseline model pretrain model fairmot_dla34.pth Google Reference this link Model save structure
${FAIRMOT_ROOT} └——————models └——————fairmot_dla34.pth └——————... └——————src └——————... └——————video └——————video └——————demo └——————result.txt └——————frame image └——————output.mp4 └——————...
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Use FairMOT Object Detection
cd ${FAIRMOT_ROOT(Object Detection)} cd src python demo.py mot --load_model ../models/fairmot_dla34.pth --conf_thres 0.4
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result.txt output
YY-MM-DD hh:mm:ss object label
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Use FairMOT position
cd ${FAIRMOT_ROOT(Position)} cd src python demo.py mot --load_model ../models/fairmot_dla34.pth --conf_thres 0.4
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result.txt output
frame_number Object id position x position y
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Use TrainModel.py
python TrainModel.py
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input
- CSI Data
- Object Detection Result.txt
- Progress
- synchronize csi label and Result.txt
- Use Randomforest