Visual Object Tracking Based on Backward Model Validation

Appearance model updating is a challenging task in visual object tracking with occlusion and appearance variation. To avoid error accumulation in model updating, validation of updating is generally performed in tracking algorithms. These algorithms use the existing appearance model to validate incoming data. However, the existing appearance model may not be able to distinguish the valid training data (resulting from large appearance variation) from the invalid ones (resulting from occlusion), since both appearance variation and occlusion would lead to a good deal of appearance change of the estimated tracking result. The root of the problem is: the existing (outdated) model with information from frame 1 to n-1 may not be able to predict large appearance variations in frame n and, as a result, the appearance variations may be excluded from model updating. This defeats the purpose of model updating, which is to include new changes in appearance variations to the model, because the existing methods do not have the provision to include such changes in model updating by validating changes with the outdated model. To address this problem, we propose a backward model validation-based visual tracking (BVT) algorithm, which performs model updating first in frame n and then uses the information from the incoming frame (frame n + 1) to backward-check whether the updating is valid (occurrence of appearance variation) or invalid (occurrence of occlusion). In this way, the uncertainty of validating unpredictable features with the existing appearance models can be avoided. Moreover, an adaptive feature fusion method is designed to properly integrate the color-based feature with texture-based feature. The proposed feature extraction method provides a robust representation of the target with both rotation and shape deformation. Experimental results demonstrate that the proposed BVT algorithm outperforms the relevant existing algorithms on both publicly available and proprietary dat- bases.