Research on Recognition Technology of Human Lower Limbs Feature Based on the Random Forest Algorithm

Liu, Yankai; Yu, Ming

doi:10.1007/978-981-10-3229-5_75

Cited by 1 publication

(1 citation statement)

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“…The common scanning position based on lidar is human legs. Many researches carry out recognition by extracting effective features of human leg shape, Liu Y obtained detection results with high accuracy by extracting many different features of human legs and using random forest algorithm, but false recognition of objects similar to human legs can occur [12]. Woojin defined geometric attributes based on LiDAR data and generalized common attributes of human legs using support vector data description [13].…”

Section: Introductionmentioning

confidence: 99%

The Detection and Following of Human Legs Based on Feature Optimized HDBSCAN for Mobile Robot

Liu

Yao

Dai

et al. 2022

J. Phys.: Conf. Ser.

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Accurate target recognition and stable follow in real clutter environment are important tasks for following a random robot. In this paper, a scheme of detecting and tracking human legs using a single lidar is proposed. The HDBSCAN algorithm is optimized by human leg characteristics, and the leg line features are fused on the basis of the density characteristics of the leg laser radar data, and the leg information of the target is clustered and its location is obtained. Based on the relative shift angle of target moving and the lock-in algorithm of tracking distance, the single target detection and tracking in unstructured environment can be realized. The lidar dataset in multiple scenes verifies that the clustering algorithm based on feature optimization improves the accuracy of target legs detection by more than 10% compared with the original algorithm. The scheme performs follow-up experiments on a self-built solid robot. The experimental results show that the robot can achieve accurate and stable human target follow in clutter environment.

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Section: Introductionmentioning

confidence: 99%