Accurate and Efficient Frame-based Event Representation for AER Object Recognition

As a new type of vision sensor with high temporal resolution, high dynamic range and low power consumption, event cameras are increasingly used in the field of computer vision. Since the output of an event camera is a sparse and discrete event stream and individual event contains very little information, it is crucial to convert the event stream into a suitable event representation to facilitate the extraction of features and the recognition of the objects. In order to construct clear and complete features from raw event data efficiently, this paper proposes a frame-based three-channel event representation method, in which the temporal channels take the timestamp of the latest event at each pixel as its value to avoid contour overlapping caused by object motion and summarizing timestamps, and an event-count channel is constructed based on the number of events at each pixel to retain the overall spatial distribution of object contours. An outlier processing is also proposed to avoid “feature disappearance” based on the statistical distribution of the event count. We validate our method on several public event-based datasets and compare it with the existing state-of-the-art event representation methods, experimental results show that our method achieves the best classification accuracy with a lower time complexity, and it also yields a high level of robustness to latency.