Video vehicle detection method for adaptive learning

Abstract

The invention discloses a video vehicle detection method for adaptive learning. The video vehicle detection method for the adaptive learning treats a video vehicle detection problem as a mode classifying problem, mainly comprises an image feature extracting step, a classifier off-line training step, a classifier on-line optimizing step and a vehicle counting step, and comprises the following specific steps of firstly extracting a plurality of discriminative image features from a monitoring video, wherein the image features can be used for discriminating vehicles and backgrounds and also comprise environment information associated with light and weather conditions; secondly off-line training a mode classifier by utilizing a supervised learning method, and also online optimizing the mode classifier to automatically adjust the structure and the parameter of each component classifier, so that the classifier has the adaptive learning capability and the better classifying effect is obtained in a complex traffic scene; and finally carrying out post-process on a classifying result sequence to further improve the vehicle detecting and counting precision. The video vehicle detection method for the adaptive learning disclosed by the invention has the advantages of reinforcing the traditional virtual coil vehicle detection method, having a remarkable engineering application value and being capable of facilitating the development of the video monitoring field and the intelligent traffic field.

Description

technical field [0001] The invention belongs to the field of video monitoring technology and intelligent transportation technology, in particular to an adaptive learning video vehicle detection method. Background technique [0002] With the development of video surveillance technology, video cameras have been widely used to monitor various environments, areas and places. With the rapid increase in the number of video cameras, the traditional manual monitoring methods are far from meeting the needs of large-scale monitoring. Therefore, the realization of an intelligent monitoring method that can replace the work of human eyes has become a research focus in the field of video monitoring. At present, in the research of intelligent monitoring, the features used for automatic detection and tracking of vehicle targets mainly include vehicle texture features, contour features, edge features, etc. These features belong to the features of a single frame image in the video. Only usi...

AI Technical Summary

Problems solved by technology

However, this method is not flexible enough to switch between daytime and nighttime detection modes during actual operation; in addition, it is very difficult to accurately segment the foreground and headlight areas, which cannot meet the requirements of the pattern classifier for sample input features

[0006] Although there are already Autoscope, Iteris, Traficon and other video detection products based on the virtual coil method on the market, evaluation studies have shown that these commercial products only perform well under specific environmental conditions. In the unfavorable situation, the accuracy and robustness of the detection algorithm need to be further improved

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