In the area of multimedia semantic analysis and video retrieval, automatic object detection techniques play an important role. Without the analysis of the object-level features, it is hard to achieve high performance on semantic retrieval. As a branch of object detection study, moving object detection also becomes a hot research field and gets a great amount of progress recently. This paper proposes a moving object detection and retrieval model that integrates the spatial and temporal information in video sequences and uses the proposed integral density method (adopted from the idea of integral images) to quickly identify the motion regions in an unsupervised way. First, key information locations on video frames are achieved as maxima and minima of the result of Difference of Gaussian (DoG) function. On the other hand, a motion map of adjacent frames is obtained from the diversity of the outcomes from Simultaneous Partition and Class Parameter Estimation (SPCPE) framework. The motion map filters key information locations into key motion locations (KMLs) where the existence of moving objects is implied. Besides showing the motion zones, the motion map also indicates the motion direction which guides the proposed "integral density" approach to quickly and accurately locate the motion regions. The detection results are not only illustrated visually, but also verified by the promising experimental results which show the concept retrieval performance can be improved by integrating the global and local visual information.