Anti-occlusion particle filter object-tracking method based on feature fusion

摘要

A new anti-occlusion particle filter object-tracking method based on feature fusion is proposed in this study. Colour and local binary pattern features are extracted and additively fused with a deterministic coefficient, which is calculated based on the difference between the object features and the background. An integral cumulative histogram is proposed to reduce the computational cost of feature extraction. A new occlusion determination method is proposed, and corresponding tracking strategies are also put forward for various occlusion conditions; in the case of partial occlusion, block tracking is carried out, and in the case of serious occlusion, the least-square method is used to predict the object position. Context Aware Vision using Image-based Active Recognition (CAVIAR) and Video Image Retrieval and Analysis Tool (VIRAT) video libraries are used to validate the method. The experimental results show that the proposed method can describe an object effectively and improve tracking stability and robustness under the occlusion conditions.