Awesome
mxnet-reinspect
MXNet version of Reinspect
Features
- No training included
- 35 fps for detection (Nvidia TITANX)
- enlarge the bbox to the whole body by scales varying from scenes to scenes
- ROI Pooling for bounding boxes to extract features
Additional Requirements
- Transfer caffemodel to MXNet using caffe_converter in mxnet/tools.
Illustration
- mxnet_track.py is the main file
- config.json describes the hyperparameters
- reinspect.json is the network architecture file
- utils is similar to that in Reinspect with redundant files moved.
- model-transfer specifies the needs to deal with the model learned in Reinspect
Process
- mxnet model load googlenet params
- mxnet model load lstm params (lstm.h5)
- output the proposals
- extract the features of bbox proposals
Note
- tracking is not included which you can refer to MOT_XCODE.
- bridging python and C++ is not included which you can refer to numpy-opencv-converter.
TODO
- add learning process to mxnet-reinspect.