(PSI2.0 is an extension dataset based on the PSI1.0 dataset.)

• 20230910: All PSI data including videos, CV annotations, and Cognitive Annotation (PSI1.0 & PSI 2.0) are public for download and future exploration! [Google Drive] [PSI Homepage] 🎇⚡
• 20230606: The official [Homepage], [GitHub], and [CodaLab (Track 1)(Track 2)(Track 3)] of the IEEE ITSS Student Competition are public.

Please refer to PSI dataset for the details of PSI dataset and data structure.

(0) Arguments

# Experimental Setting
Input: Observed video sequence + target pedestrian bounding box sequence
Output: Future trajectory prediction (bounding boxes sequence)
Observed sequence length: 15 frames (0.5s for 30 fps)
Predicted sequence length: 45 frames (1.5s for 30 fps)
Overlap rate: 0.9 for traingin/validation, 1 for test 
              (To sample tracks with stride length = len(observed_sequence_length) * overlap rate
Video Splits: 
    ('./splits/PSI200_split.json')
        - Train: Video_0001 ~ Video_0110
        - Val: Video_0111 ~ Video_0146
        - Test: Video_0147 ~ Video_0204
    ('./splits/PSI100_split.json')
        - Train: Video_0001 ~ Video_0082
        - Val: Video_0083 ~ Video_0088
        - Test: Video_0089 ~ Video_0110

(1) Generate database

./database/create_database(args)

Organize the data into format as:

db = {
    - *video_name*: { # video name
        - *pedestrian_id*: { # target pedestrian name
            - 'frames': [0, 1, 2, ...], # list of frames that the target pedestrian appear
            - 'cv_annotations': {
                - 'track_id': *track_id*, # target pedestrian's track_id, obtained from raw annoation
                - 'bbox': [[xtl, ytl, xbr, ybr], [], [], ...], # list of bounding boxes of the target pedestrian appeared
            },
            - 'nlp_annotations': {
                - *annotator_id*: { # annotator's id/name
                    - 'intent': [], # list of crossing itnent at speific frame, extended from key-frame annotations 
                    - 'description': [], # list of explanation of the intent estimation for every frame from the current annotator_id
                    - 'key_frame': [] # if the specific frame is key-frame, directly annotated by the annotator. 0-NOT key-frame, 1-key-frame
                },
                ...
            }
        }
    }
}

Pedestrian trajectory ground-truth:

The ground-truth trajectory is the sequence of positions (bounding boxes) of the target pedestrian in the future time steps.

(2) training / validation / test split

Our splits are provided in ./splits. Specifically, for PSI100, all videos are splited into train/val/test as ratio $0.75:0.05:0.2$. For PSI200, we take the first 110 videos (same as all PSI100 data) as training set, video_0111 ~ video_0146 as validation, and the rest 50 videos are for test.

(3) Run training

python main.py

(4) Evaluation Metrics

ADE: Average Displacement Error
FDE: Final Displacement Error
ARB: 
FRB: 

(4). Environment

Python 3.8
PyTorch 1.10.0 + Cuda 111
Tensorboard 2.10.1

(5) Notes

This baseline only take the bounding boxes sequence of the target pedestrian as input. However, PSI contains various multi-modal annotations and information available for further exploration to contribute to the intent prediction. E.g., Video sequence, other road users bounding boxes, detailed text-based explanation annotations, etc.

[1] Tina Chen, Taotao Jing, Renran Tian, Yaobin Chen, Joshua Domeyer, Heishiro Toyoda, Rini Sherony, Zhengming Ding. "Psi: A pedestrian behavior dataset for socially intelligent autonomous car." arXiv preprint arXiv:2112.02604 (2021).

[2] Chen, Tina, Renran Tian, and Zhengming Ding. "Visual reasoning using graph convolutional networks for predicting pedestrian crossing intention." In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3103-3109. 2021.

Please feel free to send any questions or comments to psi.intent.benchmark@gmail.com