g-morgen / vec_vad Goto Github PK

by Guang Yu, Siqi Wang, Zhiping Cai, En Zhu, Chuanfu Xu, Jianping Yin, Marius Kloft. In ACM Multimedia 2020.

• python 3.6
• PyTorch 1.1.0 (0.3.0 for calculating optical flow)
• scikit-learn 0.21.3
• cuda 9.0.176
• cudnn 7.0

Download datasets from OneDrive or BaiduYunPan (code:i9b3), and move them into ./raw_datasets.

(1) Follow the instructions to install FlowNet2, then download the pretrained model flownet2, and move the downloaded model FlowNet2_checkpoint.pth.tar into ./FlowNet2_src/pretrained.

(2) Run calc_img_inputs.py: python calc_img_inputs.py. This will generate a new folder named optical_flow containing the optical flow of the different datasets.

(1) Follow the instructions to install mmdetection, then download the pretrained object detector Cascade R-CNN, and move it to ./obj_det_checkpoints.

(2) Select the model in ./data/raw2flow, and move the files in the folders (such as avenue_model_5raw1of_auc0.902) into ./data/raw2flow.

(3) Edit the file config.cfg: i. Change the dataset_name (UCSDped2, avenue and ShanghaiTech are optional) of [shared_parameters] for the selected model in step (2). ii. Change the context_of_num (4 and 0 are optional, 4 corresponds to the model with 5of and 0 corresponds to 1of) in [SelfComplete].

(4) Run test.py: python test.py.

Edit the file config.cfg according to your requirements and run train.py: python train.py.