Pedestrian Identification and Tracking within Adaptive Collaboration Edge Computing.

Nowadays, video surveillance is widespread used to achieve security life in the construction of smart city. As a result, prevalence of video surveillance equipments and technologies enables pedestrian identification and tracking to be research hotspots in the field of computer vision, whose development and application are of great significance to the construction of a good social security environment. However, pedestrian identification and tracking in monitoring scenarios still have problems of low recognition accuracy and high model complexity, and computer vision based adaptive recognition methods still have a large room for improvement and development. To address this issue, real-time pedestrian identification and tracking within adaptive collaboration edge computing environment is investigated in this paper. Within the paradigm of edge computing, the Raspberry Pi 3B+ acting as the edge computing node is adopted to handle the related issues of pedestrian identification and tracking. The combination of HOG (Histogram of Oriented Gradient) and SVM (Support Vector Machine) is investigated to achieve pedestrian identification, where, HOG is employed as the feature descriptor and SVM is employed as the classification algorithm. Furthermore, the pixel-based visual tracking algorithm is investigated to achieve effective and uninterrupted pedestrian tracking. By implementing a prototype on Raspberry Pi 3B+ using OpenCV libraries, the experimental results finally have been shown to demonstrate the efficiency of the investigations.