The paper presents a real-time compressive tracking algorithm that addresses the challenges of pose variation, illumination change, occlusion, and motion blur in object tracking. The proposed algorithm uses a non-adaptive random projection to extract features from multi-scale image spaces, preserving the structure of the image feature space. A sparse measurement matrix is used to efficiently compress foreground and background samples, and the tracking task is formulated as a binary classification problem using a naive Bayes classifier with online updates in the compressed domain. The algorithm is designed to be simple, efficient, and robust, outperforming state-of-the-art methods in terms of accuracy, robustness, and speed on challenging sequences. The effectiveness of the algorithm is demonstrated through experiments on various sequences, including those with significant pose changes, occlusions, and abrupt motions.The paper presents a real-time compressive tracking algorithm that addresses the challenges of pose variation, illumination change, occlusion, and motion blur in object tracking. The proposed algorithm uses a non-adaptive random projection to extract features from multi-scale image spaces, preserving the structure of the image feature space. A sparse measurement matrix is used to efficiently compress foreground and background samples, and the tracking task is formulated as a binary classification problem using a naive Bayes classifier with online updates in the compressed domain. The algorithm is designed to be simple, efficient, and robust, outperforming state-of-the-art methods in terms of accuracy, robustness, and speed on challenging sequences. The effectiveness of the algorithm is demonstrated through experiments on various sequences, including those with significant pose changes, occlusions, and abrupt motions.