Vehicle flow monitoring image detection and tracking system and method based on multi-feature fusion

Abstract

The invention discloses a traffic flow monitoring image detecting and tracking system and method based on multi-features fusion. The method comprises the following steps: carrying out background modeling to a first frame of image by adopting a VIBE (Visual Background extractor) algorithm, comprising a current frame to segment image targets in the frame, and updating a background model; removing noise interference from the segmented image targets by adopting color features and SILTP (Scale Invariant Local Ternary Pattern) textural features, extracting LBP (Local Binary Pattern) partitioning target image texture features, calculating a histogram of the LBP to generate the feature vector of the whole image, and calculating the main direction and the angular point descriptor of a target image by applying a FAST (Features from Accelerated Segment Test) algorithm; and calculating the histogram and the feature operator of the target image, automatically adjusting a search window, calculating image feature similarity match by applying a Mean Shift algorithm or a Lalman filtering algorithm, calculating the center position of a new window, comparing and determining a search target, and marking the search target or uploading a traffic flow detection record. The invention has the advantages of being high in detection accuracy, high in tracking speed and favorable in application prospect.

Description

technical field [0001] The invention belongs to the technical field of video image processing and pattern recognition, and relates to a vehicle flow monitoring image detection and tracking system and method based on multi-feature fusion. Background technique [0002] Traffic monitoring and automatic vehicle tracking technologies have a wide range of application values ​​in the fields of highway vehicle driving safety and intelligent transportation. Since the current traffic video monitoring system can only simply process and record images, or judge the road condition information of each road section in the monitoring scene through the operator watching the video playback, on the one hand, manual monitoring is more error-prone, and it also brings higher labor costs. , and it is impossible to process a large amount of video information in time and quickly, realize optimal traffic scheduling, monitor road condition information in real time, and record violating vehicles. There...

AI Technical Summary

Problems solved by technology

[0004] Traditional image detection and tracking methods, such as image processing methods based on statistics, background difference method, optical flow field method and time difference method, are difficult to achieve high accuracy

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