A Pedestrian Feature Extraction and Representation Method Based on Area Sparse Integral Channel

Abstract

The invention provides a pedestrian feature extraction and representing method based on a region sparse integration passage. The method includes the steps that firstly, spatial arrangement information, which is evenly sampled at random, of regions and sub-regions is used; secondly, feature passage values, inheriting various kinds of heterogeneous source information, in the sub-regions are extracted; thirdly, features of the sub-regions are fused into features of the regions; finally, all the features of the regions are connected in a cascaded mode to obtain pedestrian descriptors. By means of the pedestrian feature extraction and representing method, the dilemma choice problem in pedestrian feature extraction and representing is solved under a unified framework. The spatial arrangement information and the various kinds of heterogeneous source information are included. According to the pedestrian feature extraction and representing method, the fine-grained spatial arrangement information of the regions and the sub-regions is used, the various kinds of heterogeneous source information in the sub-regions is naturally integrated, and therefore consideration is given to descriptiveness and flexibility.

Description

technical field [0001] The invention belongs to the technical field of computer vision and relates to feature extraction and representation technology in pedestrian detection. Background technique [0002] Pedestrian detection and tracking is one of the hot research topics in the field of computer vision in recent years. Its purpose is to accurately and real-time find the human body or part of the human body or track pedestrians in the video image sequence, which is the basis and key step for the realization of human activity analysis. It integrates knowledge in many related fields such as image processing, pattern recognition, artificial intelligence and automatic control, and has broad application prospects and potential economic value in the fields of vehicle assisted driving, intelligent video surveillance, robot vision and advanced human-computer interaction. In practical applications, due to factors such as the complexity of the scene, the change of perspective and sca...

AI Technical Summary

Problems solved by technology

When using integral image technology, extracting features from a small area is easy to provide good local positioning capabilities, but is susceptible to various changes; extracting features from large areas can tolerate more changes, but lacks accurate positioning capabilities

The underlying features are strong in real-time but weak in discrimination, mixed features are strong in discrimination but poor in real-time, and learning-based features are too dependent on the selection of samples

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