Proposal selection for Tracking (i evaluating all the proposals in each frame with multiple cues to select the best one use three cues, detection detection confidence score, objectness measures computed with object edges and motion boundaries first use the normalized detection confidence score computed for each proposal box with the svM learned from the object annotation in the first frame and updated during trackin This provides information directly relevant to the object of interest in a en sequence
Proposal Selection for Tracking (iii) evaluating all the proposals in each frame with multiple cues to select the best one use three cues, detection detection confidence score, objectness measures computed with object edges and motion boundaries. first use the normalized detection confidence score computed for each proposal box with the SVM learned from the object annotation in the first frame and updated during tracking. This provides information directly relevant to the object of interest in a given sequence
Proposal Selection for trackin (iv)updating the detector model having computed the best proposal containing the object, use it as a positive exemplar to learn a new object model
Proposal Selection for Tracking (iv) updating the detector model having computed the best proposal containing the object, use it as a positive exemplar to learn a new object model