We present a real-time distributed system for tracking with non-overlapping camera views. Each camera performs multiobject tracking, and cameras communicate with each other in a peer-to-peer manner for consistent labeling. To match objects across non-overlapping views, we employ multiple features, namely color histogram, height, travel time and speed. First, camera configuration and reference values of different features are learned in the training phase. Then, we combine multiple evidences by computing an overall similarity score, which is a weighted sum of the similarity scores of different features. Communication and frame processing run in parallel and share memory. Experimental results show the success of the presented system in real-time tracking with nonoverlapping cameras and in handling merge cases.