通过使用序列图像拼接来识别暴力视频
Yueh-Shen Tu1, Yu-Shian Shen2, Yuk Yii Chan2
1Department of Electrical Engineering, Chang Gung University, Taoyuan 33302, Taiwan.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
本研究引入了一种新的方法,用于识别使用顺序图像拼接 (SIC) 与MLP-Mixer模型的暴力活动. 这种方法有效地识别暴力行为,使用的数据和计算能力比变压器模型少.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 行为识别 行为识别
背景情况:
- 变压器模型擅长行为识别,但需要大量的数据.
- 现有的暴力行为数据集不足以进行变压器训练.
- 变压器可以是计算密集型,可能会错过时间动态.
研究的目的:
- 利用有限的数据开发一种有效的暴力行为识别方法.
- 在行为分析中解决变压器模型的计算和数据限制.
- 改善暴力活动中时间特征的识别.
主要方法:
- 提出了一个新的数据集格式:顺序图像拼接 (SIC).
- 使用MLP-Mixer架构进行行为识别.
- 在各种公共数据集上训练模型,包括曲棍球战斗,CCTV暴力和现实生活情况.
主要成果:
- 与SIC一起训练的MLP-Mixer模型在暴力行为识别方面取得了高绩效.
- 与最先进的模型相比,拟议的SIC方法需要更少的参数和更少的计算能力 (FLOP).
- 在暴力行动中表现出对时间特征的有效理解.
结论:
- 连续图像拼接 (SIC) 数据集与MLP-Mixer相结合,为暴力行为识别提供了一个有效的解决方案.
- 这种方法克服了与变压器模型相关的数据稀缺性和计算挑战.
- 该方法对现实世界的安全应用程序具有前景,需要强大的行为分析.
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