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Human-Object Interaction in Videos (ST-HOI/VidHOI)

ST-HOI: A Spatial-Temporal Baseline for Human-Object Interaction Detection in Videos<br> [Paper] [Slides] [Video]<br><br> Meng-Jiun Chiou¹, Chun-Yu Liao², Li-Wei Wang², Roger Zimmermann¹ and Jiashi Feng¹<br> ¹National University of Singapore ²ASUS Intelligent Cloud Services<br><br> appears at ACM ICMR 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval

ST-HOI is a strong, spatial-temporal-aware human-object interaction (HOI) detection baseline. To take into account accurate spatial-temporal information, ST-HOI exploits trajectory-based features including correctly-localized visual features, spatial-temporal masking pose features and trajectory features.

VidHOI is one of the first large-scale video-based HOI detection benchmark. Note that in contrast to action detection datasets such as AVA/Kinetics, the interacting objects are explicitly annotated in VidHOI. We sampled and transformed video HOIs (i.e., image HOIs in continuous frames) from an existing video dataset, VidOR.

Note that each experiment was performed with eight NVIDIA Tesla V100 GPU with 32G memory. Before running the training commands ensure that your GPUs have enough memories. Otherwise, you might need to reduce the batch size accordingly. In contrast, only 1 GPU with less than 4GB GPU is used for validation commands as we evaluate with batch size of 1.

To-dos

• Automate evaluation process (instead of using vidor_eval.ipynb)
• Clean visualization tools

Installation

1. Create a conda environment

conda create -n vidhoi python=3.6 scipy numpy
conda activate vidhoi

2. Install PyTorch 1.4.0 and torchvision 0.5.0 following the official installation guide
3. Install other requirements via pip install -r requirements.txt (Note: remove torch/torchvision/torchaudio and other mismatched package requirements before proceeding!)
4. Install SlowFast and detectron2 following the instructions in OLD_README.md. (Note: skip the step of cloning a detectron2_repo/ from FacebookResearch. Install our provided detectron2_repo/ in this repository.)

Download VidHOI Benchmark

Please refer to Section 4.1 of our paper for more detail about the proposed benchmark.

First, download the original VidOR dataset and annotations from the official website and unzip to $ROOT/slowfast/dataset/vidor-github. To download VidHOI (i.e., HOI-specific) annotations, refer to files under the same folder in this repoistory, and for larger files, download them from here.

Files

• Sampled frame lists
  • frame_lists/train.csv
  • frame_lists/val.csv
• Human/object frame-wise annotations for training/validation
  • train_frame_annots.json
  • val_frame_annots.json
• Human/object trajectories for training/validation
  • train_trajectories.json
  • val_trajectories.json
• For removing testing frames with missing predicted boxes (during evaluation with precomputed boxes; details below)
  • val_instances_predictions_train_small_vidor_with_pseudo_labels.pth

One then needs to extract frames from VidOR videos using $ROOT/slowfast/dataset/vidor-github/extract_vidor_frames.sh.

Notes (Important!)

Since ST-HOI baselines that are evaluated with predicted trajectories (during validation) miss bounding boxes for some validation frames, to make their results comparable with the results using ground truth boxes, we remove those testing frames that no any bounding box got predicted by the trajectory generation model, i.e., we evaluate our all baselines only on those testing frames with at least one predicted boxes. This results in 168 less testing examples (22,967 -> 22,808 frames). Moreover, for models with Spatial-Temporal Masking Pose Module, further 1,050 out of 22,808 testing frames cannot be used as our human pose estimation model doesn't output any valid, predicted human pose. For fair comparisons, we only evaluate on the final 21,758 frames. This is done by changing the default value of VIDOR.TEST_PREDICT_BOX_LISTS from val_frame_annots.json to val_instances_predictions_train_small_vidor_with_pseudo_labels.pth. To validate models on all 22,967 frames (with ground truth trajectories), pass

• VIDOR.TEST_PREDICT_BOX_LISTS val_frame_annots.json
• VIDOR.TEST_GT_LESS_TO_ALIGN_NONGT False

to configs when starting a validation session.

Download ST-HOI Baselines

Files

To reproduce results of ST-HOI baselines, please download essential files from here and put (after unzipping, if applicable) the files to the same folder (vidor-github) as above.

Note that if you'd only like to testing with ground truth trajectories, you only need to download human_poses.zip!

• det_val_trajectories.json: detected trajectories (validation split)
• det_val_frame_annots.json: detected frame-wise annotations (validation split)
• Human poses
  • human_poses.zip: generated human poses using ground truth boxes/trajectories
  • human_poses_detected-bboxes.zip: generated human poses using detected boxes/trajectories
  • vidor_training_3d_human_poses_from_VIBE.pkl: (optional) 3D human poses generated with VIBE (training split)
  • vidor_validation_3d_human_poses_from_VIBE.pkl: (optional) 3D human poses generated with VIBE (validation split)
• detection_results.zip: raw detected boxes results (optional as it's been transformed into det_val_trajectories,json and det_val_frame_annots.json)
• vidvrd-mff.zip: (optional) the top-1 solution in Relation Understanding in Videos ACM MM 2019 Grand Challenge which includes the detected human/object trajectories used in our project. This zip file is the same as the file here.

Note that for the Detection results in Table 2, we evaluate the models (trained with ground truth boxes/trajectories) on detected boxes/trajectories. That's why we only need detected boxes/trajectories for VidHOI validation split.

Checkpoints

[Aug. 21, 2022] The file is now unavailable due to limited cloud space.

Trained models are provided for performance verification purpose without running training, and only 1 GPU is used during validation. Download the checkpoints from here and extract them under $ROOT/checkpoints/.

- checkpoints.zip: Final trained models' weights

Performance Validation

For the ease of verifying models' performance, we have uploaded the output json files of 2D/3D baselins and ST-HOI models (evaluated with ground truth boxes) here (under the output folder). One may directly download these files and refer to vidor_eval.ipynb for evaluation and visualization.

Experiments

First, rename the folder vidor-github under $ROOT/slowfast/dataset to vidor before running any command. The following commands use ground truth GT (Oracle mode) by default. To use detected trajectories, refer to NONGT version of each model.

Second, rename the paths in defaults.py: specifically, search for aicsvidhoi1 and replace the matched paths with yours.

For checking each model's final performance including mAP, use vidor_eval.ipynb (TODO: write an automatic evaluation script)

Image Baseline (2D Model)

• Training: Run

python tools/run_net_vidor.py --cfg configs/vidor/BASELINE_32x2_R50_SHORT_SCRATCH_EVAL_GT.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 1 DATA_LOADER.NUM_WORKERS 0 TRAIN.BATCH_SIZE 128 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False

• Validation: Run

python tools/run_net_vidor.py --cfg configs/vidor/BASELINE_32x2_R50_SHORT_SCRATCH_EVAL_GT.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 1 DATA_LOADER.NUM_WORKERS 0 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False TRAIN.ENABLE False TEST.CHECKPOINT_FILE_PATH ./checkpoints/BASELINE_32x2_R50_SHORT_SCRATCH_EVAL_GT/checkpoint_epoch_00020.pyth TRAIN.CHECKPOINT_TYPE pytorch VIDOR.TEST_DEBUG False

• NON-GT version: BASELINE_32x2_R50_SHORT_SCRATCH_EVAL_NONGT

Video Baseline (3D Model)

• Training: Run

python tools/run_net_vidor.py --cfg configs/vidor/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 8 DATA_LOADER.NUM_WORKERS 0 TRAIN.BATCH_SIZE 128 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False

• Validation: Run

python tools/run_net_vidor.py --cfg configs/vidor/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 1 DATA_LOADER.NUM_WORKERS 0 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False TRAIN.ENABLE False TEST.CHECKPOINT_FILE_PATH ./checkpoints/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT/checkpoint_epoch_00020.pyth TRAIN.CHECKPOINT_TYPE pytorch VIDOR.TEST_DEBUG False

• NON-GT version: SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_NONGT

3D + Trajectory (Ours-T)

• Training: Run

python tools/run_net_vidor.py --cfg configs/vidor/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 8 DATA_LOADER.NUM_WORKERS 0 TRAIN.BATCH_SIZE 128 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False VIDOR.TEST_DEBUG False

• Validation: Run

python tools/run_net_vidor.py --cfg configs/vidor/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 1 DATA_LOADER.NUM_WORKERS 0 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False TRAIN.ENABLE False TEST.CHECKPOINT_FILE_PATH ./checkpoints/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory/checkpoint_epoch_00020.pyth TRAIN.CHECKPOINT_TYPE pytorch VIDOR.TEST_DEBUG False

• NON-GT version: SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_NONGT_trajectory

3D + Trajectory + ToI Features (Ours-T+V)

• Training: Run

python tools/run_net_vidor.py --cfg configs/vidor/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-toipool.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 8 DATA_LOADER.NUM_WORKERS 0 TRAIN.BATCH_SIZE 128 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False VIDOR.TEST_DEBUG False

• Validation: Run

python tools/run_net_vidor.py --cfg configs/vidor/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-toipool.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 1 DATA_LOADER.NUM_WORKERS 0 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False TRAIN.ENABLE False TEST.CHECKPOINT_FILE_PATH ./checkpoints/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-toipool/checkpoint_epoch_00020.pyth TRAIN.CHECKPOINT_TYPE pytorch VIDOR.TEST_DEBUG False

• NON-GT version: SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_NONGT_trajectory-toipool

3D + Trajectory + Masking Pose Feature (Ours-T+P)

• Training: Run

python tools/run_net_vidor.py --cfg configs/vidor/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-spa_conf.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 8 DATA_LOADER.NUM_WORKERS 0 TRAIN.BATCH_SIZE 96 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False VIDOR.TEST_DEBUG False

• Validation: Run

python tools/run_net_vidor.py --cfg configs/vidor/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-spa_conf.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 1 DATA_LOADER.NUM_WORKERS 0 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False TRAIN.ENABLE False TEST.CHECKPOINT_FILE_PATH ./checkpoints/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-spa_conf/checkpoint_epoch_00020.pyth TRAIN.CHECKPOINT_TYPE pytorch VIDOR.TEST_DEBUG False

• NON-GT version: SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_NONGT_trajectory-spa_conf

3D + Trajectory + ToI Features + Masking Pose Feature (Ours-T+V+P)

Note that batch size is 112 for the this model.

• Training: Run

python tools/run_net_vidor.py --cfg configs/vidor/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-toipool-spa_conf.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 7 DATA_LOADER.NUM_WORKERS 0 TRAIN.BATCH_SIZE 112 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False VIDOR.TEST_DEBUG False

• Validation: Run

python tools/run_net_vidor.py --cfg configs/vidor/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-toipool-spa_conf.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 1 DATA_LOADER.NUM_WORKERS 0 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False TRAIN.ENABLE False TEST.CHECKPOINT_FILE_PATH ./checkpoints/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-toipool-spa_conf/checkpoint_epoch_00020.pyth TRAIN.CHECKPOINT_TYPE pytorch VIDOR.TEST_DEBUG False

• NON-GT version: SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_NONGT_trajectory-toipool-spa_conf

Optional Experiments

FastPose from AlphaPose is used. One may choose to

• clone AlphaPose to a separate directory, download the pretrained 2D human pose estimation model (e.g., fast_res50_256x192.pth) and put into pretrained_models; or
• Download AlphaPose.zip from google drive and unzip it to a separate directory

Then install and activate a new environment for AlphaPose following the installation guide, followed by running the commands:

• Demo some pictures:

./scripts/inference.sh configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml pretrained_models/fast_res50_256x192.pth ~/datasets/vidor/video/0001/4164158586.mp4 results

or

python scripts/demo_inference.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/fast_res50_256x192.pth --video ~/datasets/vidor/video/0001/4164158586.mp4 --outdir results --detector yolo --save_video

• Inference

python scripts/demo_inference_vidor.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/fast_res50_256x192.pth --outdir results_gt --detector yolo --split training --gpus 0,1,2,3,4,5,6,7 --qsize 40960 --posebatch 1280

Note that one can change --split from training to validation.

• Multi-gpu "distributed" inference

python scripts/demo_inference_vidor.py --cfg configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml --checkpoint pretrained_models/fast_res50_256x192.pth --outdir results_gt --detector yolo --split validation --gpus 7 --qsize 40960 --posebatch 1280 --start_folder_idx 0 --end_folder_idx 29

**The experiments below are not included in the main paper and only serve as record purpose.**

[Pre-requisite]

Run VIBE with the following commands at ~/VIBE to generate human poses for VidOR dataset and move the generated pose to the vidor dataset folder:

• python demo_vidor.py --output_folder output/ --gt_tracklet --mode training
• python demo_vidor.py --output_folder output/ --gt_tracklet --mode validation

Running

Then run the following:

• Training: Run

python tools/run_net_vidor.py --cfg configs/vidor/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-human_pose.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 8 DATA_LOADER.NUM_WORKERS 0 TRAIN.BATCH_SIZE 128 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False VIDOR.TEST_DEBUG False

• Validation: Run

python tools/run_net_vidor.py --cfg configs/vidor/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-human_pose.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 1 DATA_LOADER.NUM_WORKERS 0 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False TRAIN.ENABLE False TEST.CHECKPOINT_FILE_PATH ./output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_trajectory-human_pose/checkpoints/checkpoint_epoch_00020.pyth TRAIN.CHECKPOINT_TYPE pytorch VIDOR.TEST_DEBUG False

• Training:

python tools/run_net_vidor.py --cfg configs/vidor/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_relativity-feat.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 8 DATA_LOADER.NUM_WORKERS 0 TRAIN.BATCH_SIZE 128 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False VIDOR.TEST_DEBUG False

• Validation:

python tools/run_net_vidor.py --cfg configs/vidor/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_relativity-feat.yaml DATA.PATH_TO_DATA_DIR slowfast/datasets/vidor NUM_GPUS 1 DATA_LOADER.NUM_WORKERS 0 TEST.BATCH_SIZE 1 LOG_MODEL_INFO False TRAIN.ENABLE False TEST.CHECKPOINT_FILE_PATH ./output/SLOWFAST_32x2_R50_SHORT_SCRATCH_EVAL_GT_relativity-feat/checkpoints/checkpoint_epoch_00020.pyth TRAIN.CHECKPOINT_TYPE pytorch VIDOR.TEST_DEBUG False

# Clone the HRNet repo first
git clone https://github.com/leoxiaobin/deep-high-resolution-net.pytorch.git
# And download corresponding pretrained model weights

Original inference command

python inference.py --cfg inference-config.yaml --videoFile ../videos/3418738633.mp4 --writeBoxFrames --outputDir output TEST.MODEL_FILE ../models/pytorch/pose_coco/pose_hrnet_w32_256x192.pth

Revised inference script with line graph

python inference_sticks.py --cfg inference-config_w48.yaml --videoFile ../videos/3418738633.mp4 --writeBoxFrames --outputDir output/w48 TEST.MODEL_FILE ../models/pytorch/pose_coco/pose_hrnet_w48_384x288.pth

• check args.inferenceFps

Revised inference script with line graph with ground truth person bbox

python inference_sticks_vidor_demo.py --cfg inference-config_w48.yaml --writeBoxFrames --outputDir output/vidor/w48 --output_video TEST.MODEL_FILE ../models/pytorch/pose_coco/pose_hrnet_w48_384x288.pth

• set up video idx etc. inside the script

Another way for inference with gt person bbox

python tools/inference_vidor.py --cfg experiments/vidor/hrnet/w48_384x288_adam_lr1e-3.yaml DATASET.SPLIT training

Replace training to validation for generating human poses for val split.

Frequently Asked Questions

• Q: Is the evaluation different from that of image HOI datasets, such as HICO-DET?
• A: Yes, the evaluation is indeed not the same. Instead of using sigmoid-thresholding the class scores to obtain the positives as done in HICO-DET, we simply the top-100 prediction scores as the positives for evaluation.
• Q: In Table 2 of ST-HOI paper, Could you please let us know how the rare and non-rare numbers were retrieved? Shouldn't the Rare and Non-Rare numbers average to the Full mAP?
• A: For rare setting, we only evaluate less frequent HOI triplet classes, and for non-rare triplets, we only evaluate on triplet classes excluding the aforementioned, rare triplet classes. Since the number of rare triplet classes != number of non-rare triplet classes, the full mAP isn't equal to the average of the two numbers. However for defining rare/non-rare classes, we were indeed a bit tricky: we defined the triplet classes with less than 25 occurrence in the validation set are rare. Please refer to https://github.com/coldmanck/VidHOI/blob/master/vidor_eval.ipynb and search for delete_less_than_25_instances.
• Q: In our evaluation when we calculated the rare/non-rare triplets using the criterion mentioned in the paper, we got the exact same count as written in the paper. But while the non-rare mAP came close to what the paper reported, 26.2. the Rare mAP is way off to 10.15%.
• A: Please refer to the aforementioned ipython notebook for rare/non-rare triplets evaluation.

Citation

Please cite our paper if you find our proposed models and/or benchmark helpful for your work:

@inproceedings{chiou2021st,
title = {ST-HOI: A Spatial-Temporal Baseline for Human-Object Interaction Detection in Videos},
author = {Chiou, Meng-Jiun and Liao, Chun-Yu and Wang, Li-Wei and Zimmermann, Roger and Feng, Jiashi},
booktitle = {Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval},
pages = {9–17},
year = {2021},
}

Credit

This codebase is largely based on SlowFast and partially based the following repos:

• AlphaPose
• zero_shot_hoi
• PMFNet