Dynamic Detection and Tracking Based on Human Body Component Modeling

Abstract

Focus on the problem of dynamic human detection and tracking in complex scenes, a physical structure based Convolutional Nerual Network is proposed. Firstly, aiming at the modeling and analysis of the human body and its components, the human body detection algorithm adapted to complex scenes is proposed, and the convolutional neural network is designed to realize the model. Secondly, the human body tracking model based on convolutional neural network and off-line training is designed, and the human body tracking algorithm is optimized to realize fast and accurate tracking of human body. Using IOU, Euclidean distance and other algorithms, the relationship between the targets detected by the detection algorithms in two adjacent frames is established. Multi-modal fusion of multiple models using a state machine or the like, so that multiple models can work effectively at the same time. This experiment carried out simulation experiments on the bus video dataset. The experimental results show that the algorithm can effectively track the passengers who are obscured by each other on the bus, and the accuracy exceeds the current best algorithms, which proves the effectiveness of the algorithm.