id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_ho4p6lmanrfsljzmvdcbb52o4q	Francesc Serratosa	A probabilistic integrated object recognition and tracking framework	2012	33	.pdf	application/pdf	15182	2196	76	for each object from the current dynamic recognition probabilities, the previous tracking In our PIORT (Probabilistic Integrated Object Recognition and Tracking) framework, histogram from the tracking results; in that case the RGB value v(x,y) in the input image current step, the measurements of both the object mass center and area in the tracking object is occluded, the mass center is not measured on the a posteriori tracking image, The recognition and tracking results for the test sequences of our PIORT approaches For comparison purposes, tracking of the target objects in the test sequences was also Recognition and tracking results for the whole sequence using the PIORT-Neural Net probabilistic framework that integrates recognition and tracking of objects in image However, in the remaining five test sequences, the tracking method based on a neural object tracking, a PIORT method obtained the best results in nine of the ten test Serratosa, "Object recognition and tracking in video sequences: a new 	./cache/work_ho4p6lmanrfsljzmvdcbb52o4q.pdf	./txt/work_ho4p6lmanrfsljzmvdcbb52o4q.txt