Please use Python 2.7 and install required packages by
$ pip install -r requirements.txt
Please edit Keras configuration to load Tensorflow backend (we don't use Theano backend). A GPU is recommended.
Assume that you have CADP dataset at CADP_IMAGE_HOME
.
For example, for video with ID 0, the frames should be in CADP_IMAGE_HOME/000000/
.
There are unlabeled regions in each images, therefore for task like object detections or tracking, anomalous detection, we should generate images with those regions are masked out.
To generate mask images, please modify the output path CADP_MASK_HOME
in cover_crowd_far.py, and then please run:
$ python analysis/cover_crowd_far.py --anno_dir=./data/annotations
where anno_dir
is the directory containing VATIC format annotations (each file contains annotations for one video).
Please run the following command to output csv data for training/testing.
$ python analysis/generate_csv.py --anno_dir=./data/annotations/trainval/ --csv_output=./cadp_train.csv --use_mask=True
where csv_output
is the output csv files and use_mask
is the flag to specify whether to use masked images.
Please download pretrained Resnet-50 model from https://github.com/fchollet/keras/tree/master/keras/applications.
Assuming that you have put the pretrained Resnet model at MODEL_DIR/resnet50_weights_tf_dim_ordering_tf_kernels.h5
.
Please set that path as the value of cfg.base_net_weights
at L25 of train_cadp_frcnn.py.
After all above steps are done, please train Faster R-CNN with
$ python analysis/train_cadp_frcnn.py
After training, to measure the mAP@IoU=0.5, please run
$ python analysis/measure_map.py --path=./cadp_test.csv --parser=simple
Where path
is the CSV files containing the annotations of test set and parser
must be simple
to parse the annotations.
Accident Forecasting Traffic Camera
@INPROCEEDINGS{8639160,
author={A. P. {Shah} and J. {Lamare} and T. {Nguyen-Anh} and A. {Hauptmann}},
booktitle={2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)},
title={CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis},
year={2018},
volume={},
number={},
pages={1-9},}