Haar and HoG characteristic based preceding car detection method

Abstract

The invention discloses a Haar and HoG characteristic based preceding car detection method. The method includes: 1), manually selecting a large number of vehicular pictures and non-vehicular pictures as positive samples and negative samples of a training set, and formatting the positive samples and the negative samples to be under 24X24 pixel; 2), using Haar characteristics and HoG characteristics to respectively characterize each of the positive samples and the negative samples to form characteristic vectors; 3) respectively establishing a weak classifier aiming at two characteristic vectors formed according to the Haar characteristics and the HoG characteristics; 4), utilizing the cascaded Adaboost algorithm to train the weak classifiers to obtain a cascaded vehicular strong classifier; and 5), inputting sub-images of different sizes and different positions into the cascaded vehicular strong classifier for judgment according to preceding road video images obtained by a vehicular camera, and judging the sub-images where a vehicle is positioned to be a preceding vehicle. The Haar and HoG characteristic based preceding car detection method is good in instantaneity and high in robustness, and has active influences on guaranteeing vehicular safe traveling, human and property safety.

Description

[0001] Technical field [0002] The invention belongs to the field of pattern recognition, relates to the field of automobile active safety, and specifically relates to a method for detecting a preceding vehicle based on Haar and HoG features. Background technique [0003] With the progress of urbanization and the popularization of automobiles, the occurrence of traffic accidents has increased sharply, and caused a large number of injuries and economic losses. How to ensure the safe and fast driving of vehicles on highways and avoid rear-end collisions and other traffic accidents has become an important issue in the automotive field. The concept of automobile active safety came into being, and the detection of the preceding vehicle on the road has become a hot spot in the research of automobile active safety. [0004] At present, the commonly used methods for detecting vehicles in front mainly include two categories: feature-based methods and model-based methods. [0005] The featur...

AI Technical Summary

Problems solved by technology

However, due to the variety of vehicle models, the attitude of the vehicle will also change during the driving process, so that the vehicle shows various shapes, and it is difficult to establish a unified model to describe it.

At the same time, the model-based detection method relies too much on the vehicle model. Once the model matching fails, the moving vehicle cannot be correctly identified. Although the establishment of a fine vehicle model can improve the matching accuracy, it will inevitably lead to an increase in the amount of calculations in the matching process. Multiplied, difficult to meet real-time requirements

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