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Transfer Learning for Frame-based Activity Recognition

基于框架的活动识别的迁移学习

基本信息

批准号：
1941917
负责人：
金额：
--
依托单位：
University of Bristol
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1941917
关键词：
Transfer Learning Frame based Activity

项目摘要

Activity recognition is an important task for home surveillance to monitor the wellbeing of children, pets and elderly as well as for security purposes. Despite an increasing number of video monitors used in households little provide smart monitoring. Therefore, this research will be working towards visual activity recognition to detect and classify actions that can be used to determine the health of an individual.Most research towards activity recognition has focused on recognising a single high-level action in a video (e.g. playing football or making a sandwich) however in the context of home surveillance frame-based activity recognition provides more meaningful information that provide low-level actions (e.g. put down plate or pick up mug) for each frame as soon as the action occurs.State of the art methods for activity recognition incorporate Convolutional (CNN) and/or Recurrent Neural Networks (RNN). With these methods, incorporating temporal information across a video greatly improves the accuracy of recognition. Popular approaches to activity recognition include extracting features from both RGB and Optical Flow frames using CNNs used for classification, or to train Long Short Term Memory units (LSTM) on the RGB features extracted by CNNs. These techniques have shown varying success across datasets, particularly for frame based action recognition that provide only a small benefit compared to hand crafted features. This lack of success is partly due to the lack of available training data due to the difficulties in collecting and annotating datasets for actions.Transfer learning has shown to improve the accuracy of object recognition where the specific environment to test the models on has little training data. By pre-training Neural Networks on larger datasets of objects the models can learn features that are also relevant to the test environment before fine-tuning the model on test environment with little training data available. Transfer learning in the temporal domain, shown to be important for action recognition, has had little research.This work will focus on designing models to improve the accuracy of frame-based activity recognition of low level actions that transfer well to different environments that lack large amounts of training data.

活动识别是家庭监控的一项重要任务，用于监控儿童、宠物和老人的健康状况以及安全目的。尽管越来越多的家庭使用视频监视器，但很少有人提供智能监控。因此，这项研究将致力于视觉活动识别，以检测和分类可用于确定个人健康的动作。大多数活动识别的研究集中在识别视频中的单个高级动作(例如，踢足球或做三明治)，然而在家庭监视的背景下，基于帧的活动识别提供了更有意义的信息，一旦动作发生，就为每一帧提供低级别动作(例如，放下盘子或拿起杯子)。最新的活动识别方法包括卷积(CNN)和/或递归神经网络(RNN)。使用这些方法，在视频中加入时间信息可以极大地提高识别的准确性。常用的活动识别方法包括使用用于分类的CNN从RGB和光流帧中提取特征，或者根据CNN提取的RGB特征来训练长期短期记忆单元(LSTM)。这些技术在不同的数据集上显示出不同的成功，特别是对于基于帧的动作识别，与手工制作的特征相比，这种识别只提供了很小的好处。这种缺乏成功的部分原因是由于收集和注释动作数据集的困难而缺乏可用的训练数据。迁移学习已显示出在用于测试模型的特定环境中训练数据很少的情况下提高了对象识别的准确性。通过在较大的对象数据集上预先训练神经网络，模型可以学习与测试环境相关的特征，然后在测试环境中微调模型，而可用的训练数据很少。时间域中的迁移学习对动作识别具有重要意义，但研究较少，本文将重点研究如何在缺乏大量训练数据的不同环境下，提高基于帧的低层动作的动作识别准确率。