Effective Object Detection From Traffic Camera Videos
- Honghui Shi
- Zhichao Liu
- Yuchen Fan
- Xinchao Wang
- Prof. Thomas Huang
Our Implementation for Nvidia AI City Challenge
Our implementation py-rfcn is adapted from py-R-FCN, with additions and modification to support our winning solution to the 1st IEEE Smart World Nvidia AI City Challenge. (For usage and installation of the original py-R-FCN, please refer to here.)
1. Prepare dataset
$ mkdir -p CODE_DIR/data/AICdevkit/results/AIC/Main $ cd CODE_DIR/tools $ python ./preprocess.py DATASET_DIR CODE_DIR/data
2. Build from source
$ sh compile.sh $ export PYTHONPATH=$PYTHONPATH:CODE_DIR/caffe/python
3. Test on our pre-trained model
Download models from here.
$ bash test.sh 0
4. Train on aic dataset
We find it is better to train vehicle detector separately from traffic-signal detector.
if you want to train without traffic-signal
$ sh train.sh 0
if you want to train on traffic-signal
$ sh train.sh 1
5. Postprocess for submission
$ python postprocess.py CODE_DIR/data output_dir
This work is supported in part by IBM-ILLINOIS Center for Cognitive Computing Systems Research (C3SR) - a research collaboration as part of the IBM Cognitive Horizons Network.