Intelligent traffic analysis: A heuristic high-dimensional image search algorithm based on spatiotemporal probability for constrained environments

Intelligent traffic analysis: A heuristic high-dimensional image search algorithm based on spatiotemporal probability for constrained environments

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However, most heuristic algorithms are solely based on image features, due to the lack of diverse attributes of samples.Rather than rely on the existing datasets, this paper fully processes the original data.Firstly, the original videos were collected from several intelligent traffic surveillance cameras (monitoring nodes).Then, the three categories of objects in all videos were classified by a video image classifier.For each object in the critical class Basketball of cars, four attributes were identified, including ID, region of interest (ROI), sampling time and sampling position (position of sampling node).

Next, the moving attitude (i.e.moving direction and speed) of each object was estimated through moving target tracking, and added to the preprocessed data.On this basis, a basic hyper-spherical hash search algorithm was created Special Application for the initial identification of similar objects.Furthermore, 93% of the objects were accurately identified through the recognition of license plate.

Thereafter, a spatiotemporal correlation Bayesian network (STCBN) was established to predict the probability of each object to appear at a certain node in a period of time.Based on the probability, the heuristic factor was designed and used to enhance the basic hyper-spherical hash search algorithm.The enhancement improves the search performance, while reducing the environmental requirements of a single node.Therefore, the enhanced algorithm can be deployed effectively in edge computing systems.Our method was proved effective through experiment on a number of actual monitoring nodes.