Sift features based object tracking with discrete wavelet transform

Abstract

A novel first-detect-then-identify approach with SIFT features and discrete wavelet transform for tracking object is proposed in real surveillance scenarios. For accurate and fast moving object detection, discrete wavelet transform is adopted to eliminate the noises of the frames which may cause detection errors, and then objects are detected by applying the inter-frame difference method on the low frequency parts of two consecutive frames, and then SIFT feature is used for object representation and identification due to its invariant properties. Experimental results demonstrate that the proposed strategy improves the tracking performance by comparing with the classical mean shift method, and it is also shown that the proposed algorithm can be also applied in multiple objects tracking in real scenarios.