Human object interaction detection based on feature optimization and key human-object enhancement

Abstract

Aiming at the problem of unclear or missing human object interaction behavior objects in complex background, we propose a human object interaction detection algorithm based on feature optimization and key human-object enhancement. In order to solve the problem of missing human behavior objects, we propose Feature Optimized Faster Region Convolutional Neural Network (FOFR-CNN). FOFR-CNN is an object detection network optimized by multi-scale feature optimization algorithm, taking into account both image semantics and image structure. In order to reduce the interference of complex background, we propose a Key Human-Object Enhancement Network. The network uses an instance-based method to enhance the features of interactive objects. In order to enrich the interaction information, we use the graph convolutional network. Experimental results on HICO-DET, V-COCO and HOI-A datasets show that the proposed algorithm has significantly improved accuracy and multi-scale object detection ability compared with other human object interaction algorithms.