Kumar Manish, Raju Kota Solomon, Kumar Dinesh, Goyal Nitin, Verma Sahil, Singh Aman
Electronic Science Department, Kurukshetra University, Kurukshetra, Haryana India.
Central Electronics Engineering Research Institute, CSIR, Pilani, India.
Multimed Tools Appl. 2021;80(20):31277-31295. doi: 10.1007/s11042-020-10471-x. Epub 2021 Jan 20.
Smart city surveillance systems are the battery operated light weight Internet of Things (IoT) devices. In such devices, automatic face recognition requires a low powered memory efficient visual computing system. For these real time applications in smart cities, efficient visual recognition systems are need of the hour. In this manuscript, efficient fast subspace decomposition over Chi Square transformation is proposed for IoT based on smart city surveillance systems. The proposed technique extracts the features for visual recognition using local binary pattern histogram. The redundant features are discarded by applying the fast subspace decomposition over the Gaussian distributed Local Binary Pattern (LBP) features. This redundancy is major contributor to memory and time consumption for battery based surveillance systems. The proposed technique is suitable for all visual recognition applications deployed in IoT based surveillance devices due to higher dimension reduction. The validation of proposed technique is proved on the basis of well-known databases. The technique shows significant results for all databases when implemented on Raspberry Pi. A comparison of the proposed technique with already existing/reported techniques for the similar applications has been provided. Least error rate is achieved by the proposed technique with maximum feature reduction in minimum time for all the standard databases. Therefore, the proposed algorithm is useful for real time visual recognition for smart city surveillance.
智慧城市监控系统是由电池供电的轻量级物联网（IoT）设备。在这类设备中，自动人脸识别需要一个低功耗、内存高效的视觉计算系统。对于智慧城市中的这些实时应用而言，高效的视觉识别系统是当务之急。在本论文中，基于智慧城市监控系统，针对物联网提出了一种基于卡方变换的高效快速子空间分解方法。所提出的技术使用局部二值模式直方图提取用于视觉识别的特征。通过对高斯分布的局部二值模式（LBP）特征应用快速子空间分解来丢弃冗余特征。这种冗余是基于电池的监控系统内存和时间消耗的主要因素。由于具有更高的降维能力，所提出的技术适用于部署在基于物联网的监控设备中的所有视觉识别应用。基于知名数据库对所提出技术进行了验证。该技术在树莓派上实现时，对所有数据库都显示出显著的结果。已将所提出的技术与针对类似应用的现有/已报道技术进行了比较。所提出的技术在所有标准数据库中以最短时间实现了最大特征约简，并实现了最低错误率。因此，所提出的算法对于智慧城市监控的实时视觉识别很有用。
