The 'Court' dataset comes from literature[1], which is collected from court trial videos. There are 121 videos in total, 60 videos are labeled 'truth' and 61 are labeled 'lie'. The video file is in court_video, and the video frame image file is in data/court. In order to ensure the reliability of the results, we conduct 5 experiments for each model and take the average value to obtain the accuracy. In each experiment, 118 videos are divided into 99 training set and 19 test set(3 videos with obvious flaws were discarded), <train_list> and <test_list> are in /data .
We collect and publish a new dataset here called the 'Pandakill' dataset. We collected the video of Werewolf Killing game from public media resources, and edited each player's shots to form a video sample, which is available in https://www.iqiyi.com/a_19rrh9xjrl.html. There are a total of 200 video samples, and the duration of each video sample is 10-50 seconds. There are 15 players in all videos. If the player is a good person, it is marked as 'truth'. If the player is a werewolf, it is marked as 'lie'. In order to ensure the reliability of the results, we conduct 5 experiments for each model and take the average value to obtain the accuracy. In each experiment, 160 videos are divided into training set and 40 test set, <train_list> and <test_list> are in /data .The video frame image file is in data/pandakill_row, and the video frame image file is in data/pandakill after object detection.
The source code of the Multi-stream-merge model is located in the folder multi-stream.
To train a new model, use the main.py script.
The template command to produce model of Multi-stream-merge on the dataset can be:
python main.py <train_list> <test_list> <result_path> --<dataset> -b –gpus
The template command to test model of Multi-stream-merge on the dataset can be:
python main.py <train_list> <test_list> <result_path> --<dataset> -b –gpus -e --resume
For example, in order to train the Multi-stream-merge on 'Pandakill' datset:
The <train_list> is shown in /data/train_pandakill_5.txt and <test_list> is shown in /data/test_pandakill_5.txt.
We first need to extract local features through TSN:
python main.py /data/train_pandakill_5.txt /data/test_pandakill_5.txt /data/pandakill_result/ /home/glq/exp/data/pandakill --dataset pandakill -b 2 --gpus 0 1
Then fusion training to get Multi-stream-merge:
python main.py /data/train_pandakill_5.txt /data/test_pandakill_5.txt /data/pandakill_result/ /data/pandakill --dataset pandakill -b 2 --gpus 0 1 -merge --resume /data/pandakill_result/_rgb_checkpoint.pth.tar
The command to test Multi-stream-merge can be:
python main.py /data/train_pandakill_5.txt /data/test_pandakill_5.txt /data/pandakill_result/ /home/glq/exp/data/pandakill --dataset pandakill -b 2 --gpus 0 1 -merge --resume /data/pandakill_result/_rgb_checkpoint_merge.pth.tar -e
The log during the training and the testing resulting are shown in data/result_pandakill_5.
In addition to our proposed model, we have also reproduced the 3 baseline models (TSN, LSTM, 3D-CNN) in the folder baseline.
The command to produce model of TSN on the 'Court' dataset can be:
python main.py pandakill RGB /data/train_pandakill.txt /data/test_pandakill.txt --arch resnet101 --num_segments 3 --lr 0.001 --lr_steps 30 60 -b 4 --epochs 40 --dropout 0.8 --gpus 0 1
The command to produce model of LSTM on the 'Pandakill' dataset can be:
CUDA_VISIBLE_DEVICES=0,1 python main.py --batch_size 16 --video_path pandakill --annotation_path pandakill.json --result_path result_lstm_pandakill --dataset pandakill --lstm Truth
The command to produce model of 3D-CNN on the 'Pandakill' dataset can be:
CUDA_VISIBLE_DEVICES=0,1 python main.py --batch_size 16 --video_path pandakill --annotation_path pandakill.json --result_path result_3dcnn_pandakill --dataset pandakill
[1] V. Pe ŕ ez-Rosas, M. Abouelenien, R. Mihalcea, and M. Burzo. Deception detection using real- life trial data. In International Conference on Multimodal In- teraction, pages 59–66, 2015.