Anti-occlusion multi-object surveillance based on improved deep learning approach and multi-feature enhancement for unmanned smart grid safety

Abstract

Unattended substations are the basis of intelligent substations, which require remote surveillance and control. Limited by the number of visual sensors, remote manual monitoring is incomplete and inefficient. Onsite workers and intruders are easily hidden by the smart grid facility, which affects the safety surveillance of personnel and equipment. The traditional kernelized correlation filter (KCF) method has a poor ability to adapt to the practical environment. This paper presents an anti-occlusion framework on the basis of imaging techniques to solve the problem of optimized surveillance. The novelty is further strengthened by its more practical Deep Learning model and tracking methods. Firstly, a multi-feature fusion model of the HOG feature and color feature is proposed to enhance target characteristics as the target is severely blocked. Secondly, a target classifier training and fast detection method based on improved CNN is introduced. Lastly, to overcome the drawbacks of the KCF tracking algorithm, such as its inability to scale adaptive and blind updates, a new adaptive learning rate strategy is proposed for occlusion tracking. The effects on the OTB-2013 dataset demonstrate that the improved technique has better accuracy and robustness when compared to KCF methods.