Various motion detection methods have been proposed in the past decade, but there are seldom attempts to investigate the advantages and disadvantages of different detection mechanisms so that they can complement each other to achieve a better performance. Toward such a demand, this paper proposes a human action detection and recognition framework to bridge the semantic gap between low-level pixel intensity change and the high-level understanding of the meaning of an action. To achieve a robust estimation of the region of action with the complexities of an uncontrolled background, we propose the combination of the optical flow field and Harris3D corner detector to obtain a new spatial-temporal estimation in the video sequences. The action detection method, considering the integrated motion information, works well with the dynamic background and camera motion, and demonstrates the advantage of the proposed method of integrating multiple spatial-temporal cues. Then the local features (SIFT and STIP) extracted from the estimated region of action are used to learn the Universal Background Model (UBM) for the action recognition task. The experimental results on KTH and UCF YouTube Action (UCF11) data sets show that the proposed action detection and recognition framework can not only better estimate the region of action but also achieve better recognition accuracy comparing with the peer work.