School of Electrical and Electronic Engineering (EEE), Nanyang Technological University, Singapore.
IEEE Trans Pattern Anal Mach Intell. 2012 Feb;34(2):302-14. doi: 10.1109/TPAMI.2011.120.
This paper proposes an algorithm for feedforward categorization of objects and, in particular, human postures in real-time 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 line segment Hausdorff distance classifier combined with on-the-fly cluster-based size and position invariant categorization. The system can achieve about 90 percent average success rate in the categorization of human postures, while using only a small number of training samples. Compared to state-of-the-art bio-inspired categorization methods, the proposed algorithm requires less hardware resource, reduces the computation complexity by at least five times, and is an ideal candidate for hardware implementation with event-based circuits.
本文提出了一种用于前馈分类的算法，特别是针对实时视频序列中的物体和人类姿势进行分类，这些视频序列来自事件驱动的时空差分图像传感器。该系统采用了事件驱动硬件和生物启发软件架构的创新组合。事件驱动的时空差分图像传感器用于提供输入视频序列，而软件模块则提取大小和位置不变的线特征，这些特征受到灵长类视觉皮层模型的启发。检测到的线特征被组织成矢量化段。在特征提取之后，使用经过修改的线段 Hausdorff 距离分类器结合在线基于聚类的大小和位置不变分类。该系统在对人体姿势进行分类时，平均成功率约为 90%，同时只使用了少量的训练样本。与最先进的生物启发分类方法相比，所提出的算法需要较少的硬件资源，计算复杂度至少降低了五倍，是事件驱动电路硬件实现的理想候选者。
