-
Anaconda3/Miniconda3
-
Tensorflow 2.9
-
Pytorch (latest)
pip install -r requirements.txt
- MOTA format =>
<frame_id> <track_id> <x_centre> <y_centre> <w> <h> <_obj_class>
cd <PATH>/lstm_tracker/trainer/
chmod +x train.sh #(only needed the first time)
train.sh <PATH>/lstm_tracker/
Download KITTI Object Tracking Video Sequences and Save all the sequences from 0000 to 0020 in <PATH>/lstm_tracker/tracker_testing/data/
cd <PATH>/lstm_tracker/tracker/
chmod +x run_tracker.sh #(only needed the first time)
run_tracker.sh <PATH>/lstm_tracker/ <PATH>/lstm_tracker/tracker_testing/data/<FOLDER> <INT>
-
<INT>
is an integer corresponding to<FOLDER>
sequence. -
<FOLDER>
should have frame wise txt file in a sub folder namedlabels
- Open a Terminal window
cd <PATH>/lstm_tracker/yolo/
python detect.py --project <PATH>/lstm_tracker/tracker_testing/ --source <PATH>/lstm_tracker/tracker_testing/data/<FOLDER> --save-txt
- Here
<FOLDER>
is the directory which contains the videos in form of frames. - Open a new Terminal window
cd <PATH>/lstm_tracker/tracker/
chmod +x run_tracker.sh #(only needed the first time)
run_tracker.sh <PATH>/lstm_tracker/ <PATH>/lstm_tracker/tracker_testing/data/<FOLDER> -1
<FOLDER>
is same as in the previous command
- The video with GT boxes is stored in
<PATH>/lstm_tracker/tracker_testing/exp/FOLDER
and GT boxes coordinates are stored in<PATH>/lstm_tracker/tracker_testing/exp/FOLDER/labels/
(in a frame wise .txt file format) - Results of MOT are stored in
<PATH>/lstm_tracker/tracker_testing/results/FOLDER.txt
- Evaluation for KITTI Test Dataset
- Open a Terminal window
cd <PATH>/lstm_tracker
chmod +x track_eval.sh #(only needed for the first time)
track_eval.sh <PATH>/lstm_tracker/
- The Evaluation Result is displayed in the Terminal window