This paper presents the design of an embedded automated digital video surveillance system with real-time performance. Hardware accelerators for video segmentation, morphological operations, labeling and feature extraction are required to achieve the real-time performance while tracking will be handled in software in an embedded processor. By implementing a complete embedded system, bottlenecks in computational complexity and memory requirements can be identified and addressed. Accordingly, a memory reduction scheme for the video segmentation unit, reducing bandwidth with more than 70%, and a low complexity morphology architecture that only requires memory proportional to the input image width, have been developed. On a system level, it is shown that a labeling unit based on a contour tracing technique does not require unique labels, resulting in more than 50% memory reduction. The hardware accelerators provide the tracking software with image objects properties, i.e. features, thereby decoupling the tracking algorithm from the image stream. A prototype of the embedded system is running in real-time, 25 fps, on a field programmable gate array development board. Furthermore, the system scalability for higher image resolution is evaluated.