Efficient Spatio-temporal Event Representation Based on Kalman Filtering and Linear Weighted Timestamps

Event cameras, serving as innovative vision sensors, provide novel insights and approaches for image classification tasks. These cameras generate a sparse and discrete event stream, with each individual event carrying minimal information. Consequently, it becomes crucial to convert this event stream into a suitable event representation that aids in feature extraction and object recognition. In this research, we present a computationally efficient spatio-temporal event representation that not only preserves the spatio-temporal information of events in its entirety but also simplifies the computation of temporal information through linearly weighted timestamps. Furthermore, we propose an adaptive segmentation method for event streams. This method generates time bins that exhibit high robustness to motion speed by integrating both global and local distribution information of event counts. To verify the efficacy of our proposed method, we conducted experiments on three publicly available datasets. The results demonstrate that our method surpasses other methods on both N-Caltech101 and CIFAR10-DVS, with enhancements of 1.1% and 4.1% respectively, and produces competitive results on N-CARS.