一种基于低分辨红外传感器的动作识别方法

张昱彤; 翟旭平; 汪静

一种基于低分辨红外传感器的动作识别方法

上海大学特种光纤与光接入网重点实验室，上海 200444

详细信息

作者简介:
张昱彤（1996-），男，江苏盐城人，硕士研究生，主要从事基于红外图像的人体动作识别算法研究工作，E-mail：zyt164819285@163.com

中图分类号: TP319.4
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- 被引次数: 0
出版历程
- 收稿日期: 2020-11-24
- 修回日期: 2021-02-01
- 刊出日期: 2022-01-20

Activity Recognition Approach Using a Low-Resolution Infrared Sensor

Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China

摘要

摘要: 如今，世界各国人口老龄化问题日益严重，为了避免独居老人发生意外，老人日常动作监测和识别算法成为了研究热点。本文设计了一种基于低分辨红外传感器的动作识别方法，通过红外传感器采集探测区的温度分布数据，对温度分布数据进行处理，从时间、温度、形变和轨迹4个方面提取多个特征，最后通过K近邻算法对“行走”、“弯腰”、“坐下”、“站起”和“摔倒”5种动作进行分类。实验结果表明平均识别准确率可达到97%，其中摔倒动作的识别准确率为100%。
- 动作识别 /
- 特征提取 /
- 低分辨率红外传感器 /
- K近邻算法
Abstract: The worldwide problem of population aging is becoming increasingly critical. To avoid accidents involving the elderly living alone, the study of the daily activities of the elderly using recognition and monitoring algorithms has become a research hotspot. This paper proposes an action recognition approach using a low-resolution infrared sensor. The proposed approach uses an infrared sensor to collect temperature distribution data in the detection area, and then processes the temperature distribution data, extracting multiple features in the four dimensions of time, temperature, deformation, and trajectory. Finally, the K-nearest neighbors algorithm is used to identify the five poses of "walking, " "bending, " "sitting, ""standing, " and "falling." Experimental results demonstrate that the average accuracy can reach 97% and that the accuracy for falling is 100%.
- activity recognition /
- feature extraction /
- low resolution infrared sensor /
- KNN algorithm

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图 1 动作识别方法流程图

Figure 1. Flowchart of the proposed activity recognition approach

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图 2 前景提取算法结果

Figure 2. Foreground extraction results

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图 3 各动作的最大温度方差和运动持续帧

Figure 3. Maximum temperature variance and motion duration frames for each action

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图 4 人体外接矩形

Figure 4. Human body circumscribed rectangle

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图 5 垂直角度示意图

Figure 5. Vertical angle diagram

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图 6 准确率混淆矩阵图

Figure 6. Accuracy confusion matrix

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表 1 不同环境温度、安装空间下的阈值对比

Table 1. Comparison of thresholds under different ambient temperatures and installation spaces

Ambient temperature	Laboratory	Living room
22℃	1.66	1.65
26℃	1.71	1.73
30℃	1.75	1.76

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表 2 欧氏距离交叉验证结果

Table 2. Euclidean distance cross-validation results

Test: Train	K＝3	K＝5	K＝7	K＝9	K＝11	K＝13	K＝15
1:1	96.67%	96.8%	96.4%	96%	95.6%	95.2%	95.2%
1:4	97.33%	97.67%	97.33%	97%	96.67%	96.33%	96%
1:9	94.67%	95.33%	95.33%	95.33%	94%	93.33%	93.33%

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表 3 曼哈顿距离交叉验证结果

Table 3. Manhattan distance cross-validation results

Test: Train	K＝3	K＝5	K＝7	K＝9	K＝11	K＝13	K＝15
1:1	97.73%	97.73%	97.20%	96.53%	96.93%	96.53%	96.4%
1:4	98%	97.67%	97.67%	97.67%	96.67%	96%	96%
1:9	96.67%	96%	96%	96%	95.33%	95.33%	94.68%

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表 4 5种分类算法结果对比

Table 4. Comparison of the results of five classification algorithms

	KNN	SVM	RF	DT	NN
Bend	96.7%	92.8%	96.6%	97.9%	97.5%
Stand	98.3%	97.3%	95.4%	96.3%	90.9%
Sit	91.7%	75.8%	88.7%	91.3%	83.3%
Fall	100%	99.1%	98.2%	96.3%	100%
Walk	98.3%	95.6%	97.4%	96.7%	95.4%
Accuracy	97%	92.12%	95.26%	95.7%	93.42%

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表 5 3种方法结果对比

Table 5. Comparison of the results of three algorithms

	Ours	Method in paper [10]	Method in paper [11]
Bend	96.7%	88.7%	94.7%
Stand	98.3%	87.3%	93.5%
Sit	91.7%	78.8%	90.5%
Fall	100%	97.6%	96.7%
Walk	98.3%	95.4%	100%
Accuracy	97%	89.6%	95.1%

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