Object classification based on discriminable features and continuous tracking

doi:10.11772/j.issn.1001-9081.2014.05.1275

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (5): 1275-1278.DOI: 10.11772/j.issn.1001-9081.2014.05.1275

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Object classification based on discriminable features and continuous tracking

LI Zhihua¹,²,LIU Qiuluan²

1. Institute of Advanced Digital Technology and Instrument, Zhejiang University, Hangzhou Zhejiang 310027, China
2. Institute of Service Engineering, Hangzhou Normal University, Hangzhou Zhejiang 311100, China;

Received:2013-11-15 Revised:2013-12-30 Online:2014-05-30 Published:2014-05-01
Contact: LI Zhihua

连续跟踪状态下基于可分性特征的目标优化分类

李志华¹,²,李秋峦²

1. 浙江大学数字技术及仪器研究所，杭州 310027
2. 杭州师范大学国际服务工程学院，杭州 311100

通讯作者: 李志华
作者简介:李志华(1981-)，男，江西南昌人，讲师，博士，主要研究方向：图像识别、嵌入式系统；李秋峦(1990-)，男，浙江金华人，硕士研究生，主要研究方向：智能识别、计算机网络。
基金资助:
国家自然科学基金资助项目

Abstract

Abstract:

Aiming at object classification problem in heavily crowded and complex visual surveillance scenes, a real-time object classification approach was proposed based on discriminable features and continuous tracking. Firstly rapid features matching including color, shape and position was utilized to build the initial target correspondence in the whole scene, in which motion direction and velocity of the moving target were used to predict the preferable searching area in the next frame to accelerate the target matching process. And then the appearance model was utilized to rematch the occluded object without establishing the correspondence. In order to enhance the classification precision, the final object classification results were determined by the maximum probability of continuous object feature extraction and classification according to the tracking results. Experimental results show that the proposed method gets better classification precision compared with the method which do not utilized the continuous tracking，and its correct rate averagely reaches 97%. The new scheme effectively improves the performance of object classification in the complex scenes.

摘要：

针对拥塞复杂监控场景中目标的准确分类问题，提出了一种连续跟踪状态下基于可分性特征的目标优化分类方法。首先对整个场景中所有目标提取简单的颜色、形状和位置特征建立初始目标匹配，利用目标的运动方向及速率预测下帧中优先搜索区域以提高目标匹配效率，减少运算量，并对未建立对应关系的遮挡目标采用外观特征模型进行再匹配。为了提高目标分类的准确率，系统利用连续跟踪状态下目标特征的不间断提取和匹配，根据匹配最大概率决定最优分类结果。通过多种场景的实验结果表明，该方法的分类准确度比未利用连续跟踪信息的方案获得了更好分类准确度，平均达到了97%，有效改善了复杂场景中目标分类精度。

CLC Number:

TP391.4

LI Zhihua LIU Qiuluan. Object classification based on discriminable features and continuous tracking[J]. Journal of Computer Applications, 2014, 34(5): 1275-1278.

李志华李秋峦. 连续跟踪状态下基于可分性特征的目标优化分类[J]. 计算机应用, 2014, 34(5): 1275-1278.

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Object classification based on discriminable features and continuous tracking

连续跟踪状态下基于可分性特征的目标优化分类

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