sliding_window_object_detector_trainer_node¶
What is this?¶
Node to train jsk_perception/SlidingWindowObjectDetector using binary support vector machine.
The object is assigned a label of +1 and -1 otherwise. The SVM used is from the OpenCV Library with default set to RBF Kernel and 10-Fold Cross Validations.
Parameters¶
~dataset_path
(string, required)Folder name where
~object_dataset_filename
and~nonobject_dataset_filename
resides.It should end with
/
.~object_dataset_filename
(string, required)Rosbag file name of the object (positive) training set.
The bag file must contain
~object_dataset_topic
topic.~object_dataset_topic
(string, default:/dataset/roi
)Topic name of
sensor_msgs/Image
which is a set of positive training examples.~nonobject_dataset_filename
(string, required)Rosbag file name of the non-object (negative) training set.
The bag file must contain
~nonobject_dataset_topic
topic.~nonobject_dataset_topic
(string, default:/dataset/background/roi
)Topic name of
sensor_msgs/Image
which is a set of negative training examples.~classifier_name
(string, required)Path to trained SVM classifier output file.
.xml
or.yaml
format is supported.~manifest_filename
(string, default:sliding_window_trainer_manifest.xml
)Path to manifest file which contains parameters of the trainer such as trainer window size, save directory, etc.
.xml
or.yaml
format is supported.~swindow_x
(int, required)~swindow_y
(int, required)Images in training dataset are resized to this size (width, height) before training SVM.
Sample¶
roslaunch jsk_perception sample_sliding_window_object_detector_trainer.launch
and wait a few minutes until “Trained Successfully” message appears.