These files are for our monocular 3D Tracking pipeline:
requirements.txt installing list of requirements
_init_paths.py import pymot path for tracking evaluation
mono_3d_estimation.py train and test on roipool feature based 3d and tracking
mono_3d_tracking.py compute correlation by IoU and deep feature, and evaluate tracking result via pymot/
motion_lstm.py training script for lstm motion model
scripts/
init.sh builds up the environment
train_gta.sh example training script for gta
train_kitti.sh example training script for kitti
test_gta.sh example testing script for gta
test_kitti.sh example testing script for kitti
model/
dla.py, dla_up.py defines base model of 3D estimation
model.py defines our 3D network architecture
model_cen.py defines our 3D network architecture with an extra node predicts projection of 3D center
motion_model.py defines our lstm architecture
tracker_model.py defines the kalman filter and lstm tracker
tracker_3d.py, tracker_2d.py for computing correlation of objects between frames
loader/
dataset.py data loader for our mono_3d_estimation.py
dla_dataset.py data format for dla.py
gen_dataset.py generate image based features for KITTI and GTA with detection bbox match to gt, BDD format in json files
gen_pred.py prediction placeholder data generation script for BDD format
utils/
config.py defines cfg, including KITTI and GTA
bdd_helper.py helper for generating BDD format
network_utils.py Utility functions for 3D estimation
tracking_utils.py Utility functions for tracking, 3D transformation
plot_utils.py Utility functions for plot 3D bounding boxes and bird's eye view
labels.py defines dataformat for show_labels.py
tools/
convert_estimation_bdd.py converts 3D estimation to BDD format
convert_tracking_bdd.py converts tracking results to BDD format
eval_dep_bdd.py evaluates 3D estimation results using metrics for depth, orientation and center
eval_mot_bdd.py evaluates tracking results using pymot/
plot_tracking.py visualization of 3D tracklets
show_labels.py show ground truth label
visualize_kitti.py visualize and convert format to kitti txt files for devkit/
devkit/ official kitti developement kit to evaluate tracking result
object-ap-eval/ 3D AP evaluation tool
pymot/ multiple object tracking evaluation tool
lib/
make.sh create execution files
'''
Using BDD format with json files
'''
# GTA train
# 447256/457467 frames
gta_data/gta_train_list.json
# For each sequences
gta_data/train/{}_bdd.json
# GTA val with detection available
# 46250/45152 frames
gta_data/gta_val_list.json
# For each sequences
gta_data/val/{}_bdd.json
# GTA test with detection available
# 184459/181034 frames
gta_data/gta_test_list.json
# For each sequences
gta_data/test/{}_bdd.jsonCheckpoint filename is using the following format.
{SESSION}_{DATASET}_checkpoint_{EPOCH}.pth.tar# For mono_3d_estimation.py
# GTA
./checkpoint/616_gta_checkpoint_030.pth.tar
# KITTI
./checkpoint/623_kitti_checkpoint_100.pth.tar
# For mono_3d_tracking.py
./checkpoint/803_kitti_300_linear.pth