Collaborative Track Analysis, Data Cleansing, and Labeling

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Provided by: Stevens Creek Software LLC
Topic: Big Data
Format: PDF
Tracking output is a very attractive source of labeled data sets that, in turn, could be used to train other systems for tracking, detection, recognition and categorization. In this context, long tracking sequences are of particular importance because they provide richer information, multiple views and wider range of appearances. This paper addresses two obstacles to the use of tracking data for training: noise in the tracking data and the unreliability and slow pace of hand labeling.
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