Method for identifying taxicabs in real time by utilizing video images

Abstract

The invention relates to the field of vehicle identification and particularly relates to a method for identifying taxicabs in real time by utilizing video images. The method comprises the following steps of: establishing a sample library, and setting color category sets for the taxicabs; defining a color parameter boundary and an area threshold of each color category in the category sets, and training a support vector machine classifier; tracking the taxicabs to be identified in an input video frame; extracting the area of each color category in a tracking window, and comparing the area of each color category with the area threshold, wherein if the area of at least one color category in the tracking window is more than the area threshold, the tracking is finished, otherwise, the tracking continues; dividing the tracking window into a plurality of pixels moving in up, down, left and right directions, thus obtaining four sub-windows; and inputting the sub-windows and the standard tracking window into the support vector machine classifier respectively to obtain identification results, and performing polling statistics on the results. Strict experiments prove that the taxicab identification method can be used for improving the accuracy of judgment, and is high in judgment accuracy and robustness.

Description

technical field [0001] The invention relates to the field of video image-based vehicle identification methods, in particular to a video image-based real-time taxi identification method. Background technique [0002] At present, the requirements for the robustness and reliability of traffic monitoring systems in intelligent transportation systems are getting higher and higher. Since vehicle detection methods based on sensors such as ground induction coils, ultrasonic waves, and radars cannot identify vehicle types, vehicle detection based on video images And recognition technology has become an important research topic in traffic monitoring. It has important practical value in the field of computer vision navigation and traffic monitoring, and provides automatic and scientific support for the intelligent and scientific management of urban complex traffic networks. At present, the vehicle recognition methods based on video images mainly include HAAR-LIKE rectangular feature al...

AI Technical Summary

Problems solved by technology

[0015] (4) The influence of complex external environment such as scene shadows, unpredictable traffic flow interference and chaotic and complex external environment, all of which are difficult to control and predict

Since taxis account for 30% of th

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