Control and preprocessing of graphic data for effective dynamic object recognition

International Workshop "Advanced Technologies in Material Science, Mechanical and Automation Engineering – MIP: Engineering – 2019"

材料科学;机械制造;原子能学

Pakhomova, O.A.^1 ; Kravets, O. Ja^1

Voronezh State Technical University, Voronezh, Russia^1

关键词: Effective dynamics;    Feature aggregation;    General structures;    High-speed objects;    Motion detection;    Sparse features;    Static objects;    Video analytics;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/537/5/052002/pdf
DOI  :  10.1088/1757-899X/537/5/052002

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

The features of detecting static objects are considered. The methods of Sparse Feature Propagation and Dense Feature Aggregation based on the neural network approach of multi-frame end-to-end learning are described. The methods are designed to increase the efficiency of dynamic object recognition. The general structure of the video analytics system is presented. The method of increasing the accuracy of the obtained recognition results and increasing the probability of detecting high-speed objects by the built-in motion detection module is proposed.