SHORT: Segmented histogram technique for robust real-time object recognition

摘要

Object recognition is a broad area that covers several topics including face recognition, gesture recognition, human gait recognition, traffic road signs recognition, among many others. Object recognition plays a vital role in several real-time applications such as video surveillance, traffic analysis, security systems, and image retrieval. This work introduces a novel, real-time object recognition approach, namely "SHORT": segmented histogram object recognition technique. "SHORT" implements segmentation technique applied on the histogram of selected vectors of an image to identify similar image(s) in a database. The proposed technique performance was evaluated by means of two different image databases, namely the Yale Faces and Traffic Road Signs. The robustness was also assessed by applying different levels of distortion on both databases using Gaussian noise and blur, and testing distortion impact on recognition rates. Additionally, the efficiency was evaluated by comparing the recognition execution time of the proposed technique with another well-known recognition algorithm called "Eigenfaces". The experimental results revealed successful recognition on clear and distorted objects. Moreover, "SHORT" performed 4.5X faster than the "Eigenfaces" algorithm under the same conditions. Furthermore, the "SHORT" algorithm was implemented on FPGA hardware by exploiting data parallelism to improve the execution performance. The results showed that the FPGA hardware version is 28X faster than the "Eigenfaces" algorithm, which makes "SHORT" a robust and practical solution for real-time applications.