A Pedestrian Detection Method Based on Aggregated Channel Features

Abstract

The invention discloses a pedestrian detection method based on aggregation channel features and belongs to the technical field of digital image processing. The invention utilizes the pedestrian detection method based on the aggregated channel features to train multiple models, that is, large and small window models, to solve the problem of false detection and missed detection caused by using a single model. And in the window suppression process using the non-maximum value suppression algorithm, the confidence of the window detected by the large window model is improved, and the non-maximum value suppression threshold is updated according to the sliding window scale ratio of the large and small models, thereby suppressing errors. Body parts detected as small objects. Compared with the pedestrian detection method based on aggregated channel features using a single model, the present invention can effectively reduce false detection and missed detection rates, and can meet real-time requirements.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and in particular relates to a pedestrian detection method based on aggregation channel features. Background technique [0002] With the rapid development of economy and science and technology, people's requirements for quality of life are getting higher and higher, and intelligent life has become a way of life that people pursue. Among them, intelligent transportation and intelligent monitoring are important components, and they are also a major research hotspot in the field of computer vision today. Pedestrians are one of the main targets in the field of intelligent transportation and intelligent monitoring. The development of its detection technology, especially the pedestrian detection technology based on image processing, is closely related to the realization of intelligence in this field. [0003] At present, pedestrian detection methods based on image processing are mainl...

AI Technical Summary

Problems solved by technology

[0009] The model matching method based on the sliding window can only detect targets larger than the model scale. In applications that need to detect small targets, if the model scale used is large, it will cause a large number of missed detections.

[0010] Based on the above problems, simply reducing the scale of the model will cause a large number of false detection problems due to the reduction of the number of features contained in the small-scale model. At the same time, simply using a small-scale model will also cause a large pyramid feature scale and slow detection speed. Difficult to meet real-time requirements

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