结合时空拓扑特征和稀疏表达的人体行为识别算法  

Human behavior recognition algorithm with space-time topological feature and sparse expression

作　　者：黄文丽 范勇[2] 

机构地区：[1]蒲城清洁能源化工有限责任公司,陕西渭南715506 [2]西南科技大学计算机科学与技术学院,四川绵阳621010

出　　处：《计算机应用》2013年第6期1701-1705,1710,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(10676029;10776028)

摘　　要：基于视觉的行为分析是图像处理、模式识别等领域重要研究内容之一,在公共安全和军事上有广泛前景。在固定单摄像机下,针对单特征描述力欠缺、运动遮挡、空洞和阴影等问题,提出一种结合时空拓扑特征和稀疏表达的行为识别算法。该算法利用随机投影融合拓扑结构、几何不变特征和时空Poisson信息构造强内聚高区分低维的时空拓扑特征,结合模拟人脑解决问题的加噪稀疏机制,识别近景人体行为。实验结果表明时空拓扑特征比单特征的识别率高12.79%,在噪声情况下识别率仅降低6.15%,多行为识别率达到87.78%;该算法具有较强的时空特征描述力、噪声鲁棒性和识别高效性。Behavior analysis based on vision is one of the important research topics in image processing, pattern recognition, etc, and it has wide application prospects on public security and military field. For the problems of a fixed camera such as lack of the single feature description, motion occlusions, holes and shadows, the paper proposed a behavior recognition algorithm which combines space-time topological feature with sparse expression. It used random projection to get a space-time topological feature of strong cohesion, high distinction and low dimension, which fused topology structure, geometric invariant and space-time Poisson information. The noise-adding sparse mechanism resolving problems by simulating human was combined to identify behaviors of human body in a close-range monitor scene. The experimental results show that the recognition rate of space-time topological feature is 12.79% higher than that of single one. The recognition rate of this proposed algorithm is only 6.15% down in a noisy scene, and that for multi-behavior reaches 87.78%. This algorithm has the properties of strong description for space-time feature, higher robustness against noise and high efficiency for behavior recognition.

关 键 词：时空拓扑特征 稀疏表达 行为识别 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]