Human pose classification within the context of near-IR imagery tracking

Jiwan Han, A. Gaszczak, Ryszard Maciol, Stuart Barnes, T. Breckon
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引用次数: 17

Abstract

We address the challenge of human behaviour analysis within automated image understanding. Whilst prior work concentrates on this task within visible-band (EO) imagery, by contrast we target basic human pose classification in thermal-band (infrared, IR) imagery. By leveraging the key advantages of limb localization this imagery offers we target two distinct human pose classification problems of varying complexity: 1) identifying passive or active individuals within the scene and 2) the identification of individuals potentially carrying weapons. Both approaches use a discrete set of features capturing body pose characteristics from which a range of machine learning techniques are then employed for final classification. Significant success is shown on these challenging tasks over a wide range of environmental conditions within the wider context of automated human target tracking in thermal-band (IR) imagery.
近红外图像跟踪下的人体姿态分类
我们解决了在自动图像理解中人类行为分析的挑战。虽然先前的工作集中在可见光波段(EO)图像中的这项任务,但相比之下,我们的目标是热波段(红外,IR)图像中的基本人体姿势分类。通过利用肢体定位的关键优势,该图像为我们提供了两个不同复杂程度的不同人体姿势分类问题:1)识别场景中被动或主动的个体;2)识别潜在携带武器的个体。这两种方法都使用一组离散的特征来捕获身体姿势特征,然后使用一系列机器学习技术进行最终分类。在热波段(IR)图像中自动人体目标跟踪的更广泛背景下,这些具有挑战性的任务在广泛的环境条件下取得了重大成功。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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