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Background modeling and foreground object detection are crucial techniques for embedded image surveillance systems. The popular and accurate methods are mostly pixel based, taking up more memory and requiring longer execution times. Thus, these techniques are not suitable for embedded platforms. This paper presents a block-based major color background modeling and a foreground detection algorithm that possesses efficient processing and low memory requirement in a complex scene, making them feasible for embedded platforms. Our proposed approach consumes 37% less memory and increases accuracy by at least 2.5% compared to other existing methods. Last, implementing the object detection algorithm on the 2.83GHz CPU, we can achieve 26 frames per second for the benchmark video with image size 768×576.