Battery-powered wireless embedded smart cameras have limited processing power, memory and energy. Since video processing tasks consume significant amount of power, the problem of limited resources becomes even more pronounced, and necessitates designing light-weight algorithms suitable for embedded platforms. In this paper, we present a resource-efficient salient foreground detection and tracking algorithm. Contrary to traditional methods that implement foreground object detection and tracking independently and in a sequential manner, the proposed method uses the feedback from the tracking stage in the foreground object detection. We compare the proposed method with a sequential method on the microprocessor of an embedded smart camera, and present the savings in the processing time and energy consumption and the gain in the lifetime of a battery-powered camera for different scenarios. The presented method provides significant savings in terms of the processing time of a frame. We take advantage of these savings by sending the microprocessor to idle state at the end of processing a frame, and when the scene is empty.