Guest Editorial:Intelligent Video Surveillance and Related Technologies

Due to the increasing demand for developing a secure and smart living environment, the intelligent video surveillance technology has attracted considerable attention. Building an automatic, reliable, secure, and intelligent video surveillance system has spawned large research projects and triggered many popular research topics in several international conferences and workshops recently. This special issue of Journal of ElecWonic Science and Technology （JEST） aims to present recent advances in video surveillance systems which address the observation of people in an environment, leading to a real-time description of their actions and interactions.

Research on Video Object Detection Methods Based on YOLO with Motion Features

Aiming at the fixed-view video surveillance scene,this paper proposes a video object detection method that combines motion features and YOLO.The method uses the method of filtering video frames without motion features and segmenting video frames with motion features to reduce the reasoning pressure of the YOLO algorithm model.In this process,video frames containing moving objects are first obtained by the moving object detection module.Second,the moving target will be recognized by the object of interest recognition module.Finally,the background decision module records and analyzes the detection results to obtain background model updates or result output.It detects moving objects without using traditional background modeling methods.Experiments based on theCDnet2014 dataset showthat our method improves the missed detection rate by 0.098% and the average inference speed per frame by 45.62%compared with the YOLO-based humanoid detection method.Furthermore,the method has superior performance in scenarios where target objects appear less frequently(substations,transmission lines,and hazardous areas).

Crowdmodeling based on purposiveness and a destination-driven analysis method

This study focuses on the multiphase flow properties of crowd motions.Stability is a crucial forewarning factor for the crowd.To evaluate the behaviors of newly arriving pedestrians and the stability of a crowd,a novel motion structure analysis model is established based on purposiveness,and is used to describe the continuity of pedestrians’pursuing their own goals.We represent the crowd with self-driven particles using a destination-driven analysis method.These self-driven particles are trackable feature points detected from human bodies.Then we use trajectories to calculate these self-driven particles’purposiveness and select trajectories with high purposiveness to estimate the common destinations and the inherent structure of the crowd.Finally,we use these common destinations and the crowd structure to evaluate the behavior of newly arriving pedestrians and crowd stability.Our studies show that the purposiveness parameter is a suitable descriptor for middle-density human crowds,and that the proposed destination-driven analysis method is capable of representing complex crowd motion behaviors.Experiments using synthetic and real data and videos of both human and animal crowds have been conducted to validate the proposed method.