Abstract
Classification of spatiotemporal events captured by neuromorphic vision sensors or event based cameras in which each pixel senses the luminance changes of related spatial location and produces a sequence of events, has been of great interest in recent years. In this paper, we find that the classification accuracy can be significantly improved by combing random forest (RF) classifier with pixel-wise features. RF is a statistical framework with high generalization accuracy and fast training time. We uncover that random forest could grow deep and tend to learn highly irregular patterns of spatiotemporal events with low bias, and thus it is more suitable for achieving the classification objective. The experimental results on MNIST-DVS dataset and AER Posture dataset show that the RF based classification approach in this work outperforms the state of art algorithms in both classification accuracy and computation time cost.
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Li, H., Li, G., Shi, L. (2016). Classification of Spatiotemporal Events Based on Random Forest. In: Liu, CL., Hussain, A., Luo, B., Tan, K., Zeng, Y., Zhang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science(), vol 10023. Springer, Cham. https://doi.org/10.1007/978-3-319-49685-6_13
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DOI: https://doi.org/10.1007/978-3-319-49685-6_13
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