Method for detecting shielding of road region monitoring video by leaves

Abstract

The invention belongs to the field of computer vision, and particularly relates to a method for detecting shielding of a road region monitoring video by leaves. Firstly, a Mask R-CNN-based deep learning platform is established for training a network model of a road region and a tree leaf target, and then a detected road region result is optimized to conform to recognition expectation in an urban area video monitoring scene; and secondly, by displaying and understanding a position relation, on a two-dimensional plane, of a three-dimensional space in video monitoring, a judgment result of whether the road region is shielded by the leaves or not is finally obtained according to shielding rules. The method can replace a traditional manual mode of checking a large number of browsing videos oneby one to confirm the shielding by the leaves; the feature dependency with subjective empirical disagreements based on tree leaf area sizes, distribution areas, distances and the like is got rid of; and the shielding perception application of the three-dimensional space is realized by simulation in a two-dimensional space.

Description

technical field [0001] The invention belongs to the field of computer vision, and can be used in a detection system for monitoring the condition that a road area in a video picture is blocked by leaves. In particular, it relates to a detection method for road area monitoring video blocked by leaves. Background technique [0002] In the popularization and application of social public safety prevention video surveillance systems represented by "Safe City" and "Snow Bright Project", the construction of a large number of monitoring points within the urban area needs to be located on both sides of various public transportation roads. Due to the natural change of seasons, the improper use of some artificial cameras or the occurrence of accidents, there are leaves that cannot be ignored in the camera's monitoring field of view, resulting in the loss of monitoring target information on the road to varying degrees. Related business applications have caused obvious interference, and ...

AI Technical Summary

Problems solved by technology

[0003] The traditional leaf occlusion detection method generally uses manual feature extraction for leaf target recognition. Therefore, for engineers, the manual recognition ability of leaf target features poses a great challenge and is inefficient.

In terms of occlusion judgment, due to the complexity of the application of video surveillance scenes, whether the existence of leaves will cause visual interference will be affected by subjective factors. There is a lack of unified cognitive standards, and the application of prior knowledge is also limited. The overall effect is not ideal.

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