Along with such dangerous sources as big fire

explosion and toxic matter leak in the chemical plants

the visual tracking technology is a simple yet effective solution. As an effective real-time visual target tracking algorithm

the tracking-learning-detection (TLD) has drawn wide attention around the world. In this paper

we propose a prediction-tracking-learning-detection (PTLD) based visual target tracking algorithm

which is obtained by making several improvements based on the original TLD algorithm. The improvements include employing Kalman filter in the detector of TLD for estimating the location of the target to reduce the scanning region of the detector and improve the speed of the detector; adding Markov model based target moving direction predictor in the detector of TLD to increase the discretion for target with similar appearance. In addition to ascending in the tracking speed by increasing the position and speed prediction

we use the spatiotemporal analysis that also greatly improves the tracking precision. Experimental results show that the proposed PTLD algorithm provides a means for robust real-time visual tracking.