Hierarchical-Based Object Detection with Improved Locality Sparse Coding  

作　　者：ZHU Suguo DU Junping REN Nan LIANG Meiyu 

机构地区：[1]Beijing Key Laboratory of Intelligent Telecommunication software and Multimedia,School of Computer Science,Beijing University of Posts and Telecommunications

出　　处：《Chinese Journal of Electronics》2016年第2期290-295,共6页电子学报（英文版）

基　　金：supported by the National Basic Research Program of China(973 Program)(No.2012CB821200,No.2012CB821206);the National Natural Science Foundation of China(No.61320106006)

摘　　要：This paper proposes to extend the hierarchical method to be adapted to sequential frames, aiming at detecting the moving object in dynamic scenes. A novel two-layer model is proposed, in which dictionaries are learned through three different stages and the locality constrained sparse representation is improved. This leads more significant improvement for performance of both static image classification and moving object detection. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the state-of-the-art classification methods, and also able to detect moving object in the sequential frames accurately.This paper proposes to extend the hierarchical method to be adapted to sequential frames, aiming at detecting the moving object in dynamic scenes. A novel two-layer model is proposed, in which dictionaries are learned through three different stages and the locality constrained sparse representation is improved. This leads more significant improvement for performance of both static image classification and moving object detection. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the state-of-the-art classification methods, and also able to detect moving object in the sequential frames accurately.

关 键 词：Hierarchical detection Sparse coding Classification Object detection 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]