视频监控中H.264/AVC压缩域快速人体动作识别

Manu Tom, R. Venkatesh Babu
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引用次数: 4

摘要

本文讨论了一种新的H.264/AVC压缩域的高速人体动作识别方法。该算法利用量化参数的线索和压缩视频序列中提取的运动向量进行特征提取,并利用支持向量机(SVM)进行进一步分类。我们工作的最终目标是描绘一个比像素域更快的算法,具有相当的精度,仅利用来自压缩视频的稀疏信息。部分解码消除了完全解码的复杂性,最大限度地减少了计算负荷和内存使用,从而降低了硬件利用率和快速识别结果。所提出的方法可以处理光照变化、尺度和外观变化,并且在室外和室内测试场景中都具有鲁棒性。我们已经在两个基准动作数据集上测试了我们的方法,达到了85%以上的准确率。该算法对动作的分类速度(>2000 fps)大约是现有最先进的像素域算法的100倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rapid human action recognition in H.264/AVC compressed domain for video surveillance
This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of our work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can effect in reduced hardware utilization and fast recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust in outdoor as well as indoor testing scenarios. We have tested our method on two benchmark action datasets and achieved more than 85% accuracy. The proposed algorithm classifies actions with speed (>2000 fps) approximately 100 times more than existing state-of-the-art pixel-domain algorithms.
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