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The architecture and prototype applications of an embedded vision system containing a neuromorphic temporal contrast vision sensor and a DSP are presented. The asynchronous vision sensor completely suppresses image data redundancy and encodes visual information in sparse address-event-representation (AER) data. Due to the efficient data preprocessing on the focal plane, the sensor delivers high temporal resolution data at a low data rate. Hence, a compact embedded vision system using a low-cost, low-power digital signal processor can be realized. The one millisecond timestamp resolution of the AER data stream allows to acquire and process motion trajectories of fast moving objects in the visual scene. Various post processing algorithms, such as object tracking, vehicle speed measurement and object classification have been implemented on the presented embedded platform. The system's low data rate output, low power operation and Ethernet connectivity make it ideal for use in distributed sensor networks. Results from traffic-monitoring and object tracking applications are presented.