Python Implementation of ECO

Run demo

cd pyECO/eco/features/

python setup.py build_ext --inplace

pip install numpy scipy python-opencv glob pandas pillow

# if you want to use deep feature 
pip install mxnet-cu80(or 90 according to your cuda version)
pip install cupy-cu80(or 90 according to your cuda version)

cd pyECO/

python bin/demo_ECO_hc.py --video_dir path/to/video

Convert to deep feature version

uncomment eco/config/config.py at line5 and comment eco/config/config.py at line 6

Benchmark results

OTB100

Tracker	AUC	Speed
ECO_deep	68.7(vs 69.1)	6~8fps on GTX 1080 Ti
ECO_hc	65.2(vs 65.0)	40~60fps on Intel i5

Visualization Results

Note

we use ResNet50 feature instead of the original imagenet-vgg-m-2048

code tested on mac os 10.13 and python 3.6, ubuntu 16.04 and python 3.6

Reference

[1] Danelljan, Martin and Bhat, Goutam and Shahbaz Khan, Fahad and Felsberg, Michael ECO: Efficient Convolution Operators for Tracking In Conference on Computer Vision and Pattern Recognition (CVPR), 2017

nightmaredimple/pyECO