This paper describes a method for moving shadow detection using the joint histogram of multifeatures. In our method, we ﬁrst obtain the moving region by background subtraction. Then, based on the intensity feature, candidate shadow regions are extracted. Moreover, the joint histogram of intensity, color, and gradient features is constructed in candidate background and foreground regions. Furthermore, the joint histogram is backprojected to the foreground regions to yield the moving shadow likelihood image. In the end, the adaptive threshold is derived by the joint histogram of the foreground and background, and accurate shadow regions are extracted by segmenting the shadow likelihood image with this threshold. The main contribution of this paper is twofold. First, multifeatures are fused together by the joint histogram, which is a unified and simple description method for shadow detection. Second, the histogram of background and foreground was compared with backprojection. Moreover, the final result only depends on a few parameters. Unlike other approaches, our method does not make any assumption and moving shadow regions can be detected fast and accurately. Experimental results show that the proposed method is efficient and robust over a broad range of shadow types and challenging video conditions.