Visual Tracking via the Conventional Disc-Harmonic Moment Invariants and the Bayesian Inference Framework
Visual object tracking is considered among the richest tracks of the computer vision area, it plays a crucial role in numerous computer vision applications such as visual surveillance, motion analysis, activity recognition. Despite the progress made in this area, it is still difficult to build an effective monitoring tool that manages the most challenging factors. In this paper, we propose an effective visual tracking method based on two main components; the motion model and the observation model. Firstly, we model the motion model under the Bayesian inference framework to predict the probable states of the target in the incoming frame of a given image sequence. Secondly, we represent the observation model by a set of moment invariants named Conventional Disc-Harmonic Moment Invariants (CDHMIs). These moments are recognized by a great power of discrimination and are
considered among the most useful tools in image analysis and pattern recognition. Then, we include an online update scheme in order to alleviate the tracking drift problem and make the proposed tracker more robust. Numerous experiments on several challenging image sequences illustrate the effectiveness of the proposed method.
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