An effective object tracking method by combining the predicted target position with compressive tracking (CPCT) is proposed. Sparse Toeplitz projection matrices with random pitches are used to extract the compression feature of the original multi-scale Haar-like feature. Then

the distance between the sample positions and the predicted positions of the Mean Shift algorithm is used as object candidates' weights and the weighted Bayes classifier is used to determine the reliable object position. An adaptive parameter updating approach is used in the classifier training. The experimental results have shown that the average success rate of the CPCT tracking algorithm is about 27% higher than that of compressive tracking(CT)

and the tracking speed is about 0.15 second per frame on average on 20 challenging sequences. CPCT tracking algorithm is more robust and smoother compared with six state-of-the-art algorithms on 20 challenging sequences by combining the predicted target position with compressive tracking and adaptive parameter updating approach.