Semi-supervised learning-based pedestrian detection method

Abstract

The invention discloses a semi-supervised learning-based pedestrian detection method. The method includes the following steps that: the training samples of a source image set and the categories of the training samples are obtained, pedestrian labeling is performed on a part of images in a target scene image set, and training samples and sample features corresponding to target scene images are obtained; a decision-making forest is generated through training based on the training samples of the source image set, training samples of which the categories are known in the target scene image set are adopted to screen decision-making trees in the decision-making forest, and after the decision-making trees are reorganized, a new decision-making forest can be generated; the new decision-making forest is adopted to score training samples of which the categories are unknown in the target scene image set, and training samples with high confidence are labeled as pedestrian training samples; the training samples of which the categories are known in the target scene image set and the pedestrian training samples are adopted to train a neural network; and test samples are inputted into the new decision-making forest, test samples with high confidence are made to pass through the neural network, so that a pedestrian detection result is obtained. The semi-supervised learning-based pedestrian detection method is advantageous in high pedestrian detection accuracy.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a pedestrian detection method and system based on semi-supervised learning. Background technique [0002] With the development of computer vision technology, pedestrian detection is one of the current research hotspots in object detection, and has a wide range of applications in video surveillance, intelligent transportation, human-computer interaction, virtual reality and other fields. Vision-based pedestrian detection belongs to the research category of human motion analysis. Through human body detection, tracking, trajectory analysis, and behavior recognition, the system can detect abnormal events in real time and alarm, changing passive monitoring into active alarm. With the development of the big data era, computer-related technologies need to solve the challenges of big data accordingly. In addition to the difficulties of clothing changes, posture changes, and vari...

AI Technical Summary

Problems solved by technology

Among them, the method based on statistical classification is used to train the classifier in the training sample set. The number of unlabeled samples is much larger than the number of labeled samples. If only a small number of labeled samples are used, the training learning model does not have good generalization ability. , and at the same time cause a large number of unlabeled samples to be wasted. If only a large number of unlabeled samples are used, the value of the labeled samples will be ignored, and the resulting classifier will not be accurate enough

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