Resumen
Tracking of moving objects in video sequences are essential for many computer vision applications & it is considered as a challenging research issue due to dynamic changes in objects, shape, complex background, illumination changes and occlusion. Many traditional tracking algorithms fails to track the moving objects in real-time, this paper proposes a robust method to overcome the issue, based on the combination of particle filter and Principal Component Analysis (PCA), which predicts the position of the object in the image sequences using stable wavelet features, which in turn are extracted from multi scale 2-D discrete wavelet transform. Later, PCA approach is used to construct the effective subspace. Similarity degree between the object model and the prediction obtained from particle filter is used to update the feature vector to handle occlusion and complex background in video frames. Experimental results obtained from the proposed method are encouraging.