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TESTING.md

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Step 1: Prepare the data and model

We experimented on the ActEv dataset and the ETH & UCY dataset. The original ActEv annotations can be downloaded from here. Please do obtain the data copyright and download the raw videos from their website. You can download our prepared features from the project page by running the script bash scripts/download_prepared_data.sh. This will download the following data, and will require about 31 GB of disk space:

  • next-data/final_annos/: This folder includes extracted features and annotations for both experiments. Data format notes are here.
  • next-data/actev_personboxfeat/: This folder includes person appearance features for ActEv experiments
  • next-data/ethucy_personboxfeat/: This folder includes person appearance features for ETH/UCY experiments

Then download the pretrained model following instructions from here.

Step 2: Preprocess - ActEv

Preprocess the data for training and testing. The following is for ActEv experiments.

python code/preprocess.py next-data/final_annos/actev_annos/virat_2.5fps_resized_allfeature/ \
  actev_preprocess --obs_len 8 --pred_len 12 --add_kp --kp_path next-data/final_annos/actev_annos/anno_kp/ \
  --add_scene --scene_feat_path next-data/final_annos/actev_annos/ade20k_out_36_64/ \
  --scene_map_path next-data/final_annos/actev_annos/anno_scene/ \
  --scene_id2name next-data/final_annos/actev_annos/scene36_64_id2name_top10.json \
  --scene_h 36 --scene_w 64 --video_h 1080 --video_w 1920 --add_grid \
  --add_person_box --person_box_path next-data/final_annos/actev_annos/anno_person_box/ \
  --add_other_box --other_box_path next-data/final_annos/actev_annos/anno_other_box/ \
  --add_activity --activity_path next-data/final_annos/actev_annos/anno_activity/ \
  --person_boxkey2id_p next-data/final_annos/actev_annos/person_boxkey2id.p

Step 3: Test the models - ActEv

Run testing with our pretrained single model for ActEv experiments.

python code/test.py actev_preprocess next-models/actev_single_model model --runId 1 \
  --load_best --is_actev --add_kp --add_activity \
  --person_feat_path next-data/actev_personboxfeat --multi_decoder

The evaluation result should be:

Activity mAP ADE FDE
0.199 17.979 37.176

Step 4: Preprocess - ETH/UCY

Preprocess the data for training and testing. The following is for ETH/UCY experiments. We conduct leave-one-scene-out experiment therefore we need to preprocess the data once for each scene.

for dataset in {eth,hotel,univ,zara1,zara2};
  do
    python code/preprocess.py next-data/final_annos/ucyeth_annos/original_trajs/${dataset}/ ethucy_exp/preprocess_${dataset} \
    --person_boxkey2id next-data/final_annos/ucyeth_annos/${dataset}_person_boxkey2id.p \
    --obs_len 8 --pred_len 12 --min_ped 1 --add_scene \
    --scene_feat_path next-data/final_annos/ucyeth_annos/ade20k_e10_51_64/ \
    --scene_map_path next-data/final_annos/ucyeth_annos/scene_feat/ \
    --scene_id2name next-data/final_annos/ucyeth_annos/scene51_64_id2name_top10.json \
    --scene_h 51 --scene_w 64 --video_h 576 --video_w 720 --add_grid --add_person_box \
    --person_box_path next-data/final_annos/ucyeth_annos/person_box/ --add_other_box \
    --other_box_path next-data/final_annos/ucyeth_annos/other_box/ \
    --feature_no_split --reverse_xy --traj_pixel_lst \
    next-data/final_annos/ucyeth_annos/traj_pixels.lst ;
  done

Step 4: Test the models - ETH/UCY

Run testing with our pretrained single model for each scene in the ETH/UCY experiments.

for dataset in {eth,hotel,univ,zara1,zara2};
  do
    python code/test.py ethucy_exp/preprocess_${dataset} next-models/ethucy_single_model/${dataset}/ model \
      --runId 1 --load_best --person_feat_path next-data/ethucy_personboxfeat/${dataset}/ \
      --scene_h 51 --scene_w 64 ;
  done

The evaluation result should be:

Scene ADE FDE
ETH 0.86 1.94
HOTEL 0.36 0.74
UNIV 0.62 1.32
ZARA1 0.42 0.91
ZARA2 0.34 0.74