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We present an algorithm for real-time tracking of multiple pedestrians in a dynamic scene. The algorithm is targeted for embedded systems and reduces computational and storage costs by using an inexpensive kinematic tracking model with only fixed-point arithmetic representations. Our algorithm leverages from the observation that pedestrians in a dynamic scene tend to move with uniform speed over a small number of consecutive frames. We use a multimodal background modeling technique to accurately segment the foreground (moving people) from the background. We then use connectivity analysis to identify blobs in the foreground and calculate the center of mass of each blob. Finally, we establish correspondence between the center of mass of each blob in the current frame with center of mass information gathered from the two immediately preceding frames. We evaluate our algorithm on a real outdoor video sequence taken with an inexpensive webcam. Our implementation successfully tracks each pedestrian from frame to frame in real-time. Our algorithm performs well in challenging situations resulting from occlusion and crowded conditions, running on an eBox-2300 Thin Client VESA PC.