Research Project

0.MOT challenge    https://motchallenge.net/ 1.FBMS(video segmentation benchmark) https://lmb.informatik.uni-freiburg.de/resources/datasets/ 2.http://videonet.team/

1.  without gt mask (1)in mot.py class mot ( imdb ): self .mask_size = cfg.TRAIN.MASK_SIZE #MRCNN $x=28 self .binary_thresh = 0.4 #MRCNN (2) def _load_mot_annotation ( self , index ): seg_mask_inds = np.zeros((num_objs, 2 ), dtype =np.uint32) #MRCNN # save image index and object index in this image ### MRCNN (need to be tested) if cfg.TRAIN.MASK_REG: #index has form: index = "MOT16-02_000003" --> has to parse into integer number index_t=index.strip() arr = index_t.split( '_' ) index_t2= int (arr[ 0 ][ 6 :]+arr[ 1 ]) ##tranfer MOT16-02_000003 --> 2000003 seg_mask_inds[ix, : ] = [index_t2, ix+ 1 ] #instance count from 1 ### MRCNN-seg im_size=tree.find( 'size' ) im_width= int (im_size.find( 'width' ).text) #print 'im_width:',im_width im_height= int (im_size.find( 'height' ).text) #print 'im_height:',im...