Title

Self Correcting Tracking For Articulated Objects

Abstract

Hand detection and tracking play important roles in human computer interaction (HCI) applications, as well as surveillance. We propose a self initializing and self correcting tracking technique that is robust to different skin color, illumination and shadow irregularities. Self initialization is achieved from a detector that has relatively high false positive rate. The detected hands are then tracked backwards and forward in time using mean shift trackers initialized at each hand to find the candidate tracks for possible objects in the test sequence. Observed tracks are merged and weighed to find the real trajectories. Simple actions can be inferred extracting each object from the scene and interpreting their locations within each frame. Extraction is possible using the color histograms of the objects built during the detection phase. We apply the technique here to simple hand tracking with good results, without the need for training for skin color. © 2006 IEEE.

Publication Date

11-14-2006

Publication Title

FGR 2006: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition

Volume

2006

Number of Pages

609-614

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/FGR.2006.100

Socpus ID

33750837158 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33750837158

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