Human Pose Recognition Based on Depth Image Multifeature Fusion

Abstract

The recognition of human pose based on machine vision usually results in a low recognition rate, low robustness, and low operating efficiency. That is mainly caused by the complexity of the background, as well as the diversity of human pose, occlusion, and selfocclusion. To solve this problem, a feature extraction method combining directional gradient of depth feature (DGoD) and local difference of depth feature (LDoD) is proposed in this paper, which uses a novel strategy that incorporates eight neighborhood points around a pixel for mutual comparison to calculate the difference between the pixels. A new data set is then established to train the random forest classifier, and a random forest two-way voting mechanism is adopted to classify the pixels on different parts of the human body depth image. Finally, the gravity center of each part is calculated and a reasonable point is selected as the joint to extract human skeleton. The experimental results show that the robustness and accuracy are significantly improved, associated with a competitive operating efficiency by evaluating our approach with the proposed data set.

Multiscale Efficient Channel Attention for Fusion Lane Line Segmentation

“…Some recent work has explored the use of multimodal fusion for detection and segmentation tasks in autonomous driving [17,20,21]. Andreas Eitel introduced a multistage training method that effectively encodes the depth information of CNN [22], so that learning does not require large depth datasets, through the data enhancement scheme of robust learning of the depth image, it is corroded with the real noise mode [23]. Hyunggi Cho et al [20] redesigned the sensor configuration and installed multiple LiDAR pairs and vision sensors.…”