Industrial Pervasive Edge Computing-Based Intelligence IoT for Surveillance Saliency Detection

Numerous surveillance data processing is crucial in the internet-of-things systems with pervasive edge computing. In this processing, salient object detection from surveillance videos plays an important role because it provides the human-concerned semantic cue for various industrial tasks. However, it is still challenging for the existing studies with two aspects. The first one is the redundant saliency information from moving background to disturb the detection of salient objects. The second one is the difficulty to model the spatiotemporal saliency uncertainty. To overcome these challenges, an intelligent approach is proposed for surveillance saliency detection. It enables a region-proposal-based optical flow strategy to suppress the saliency enhancement of non-salient regions due to the moving background. Besides, it develops the bidirectional Bayesian state transition strategy to model the motion uncertainty for refining the spatiotemporal saliency feature. Extensive experiments have been performed on two datasets (the increase of ${F_{\beta}}$ is larger than 0.01 for DAVIS, and larger than 0.015 for UVSD), and the comparison with seven methods to evaluate the effectiveness of the proposed approach.