Abstract-This paper proposes an algorithm for feedforward categorization of objects, and in particular human postures in realtime video sequences from address-event temporal-difference image sensors. The system employs an innovative combination of event-based hardware and bio-inspired software architecture. An event-based temporal difference image sensor is used to provide input video sequences, while a software module extracts size and position invariant line features inspired by models of the primate visual cortex. The detected line features are organized into vectorial segments. After feature extraction, a modified linesegment Hausdorff-distance classifier combined with on-the-fly cluster based size and position invariant categorization. The system can achieve about 90% average successful rate in the categorization of human postures, while using only a small number of train samples. Compared to state-of-the-art bioinspired categorization methods, the proposed algorithm requires less hardware resource, reduces the computation complexity by at least 5 times, and is an ideal candidate for hardware implementation with event-based circuits.