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Deep Learning of Surveillance Video (EPSRC iCASE BAE)

监控视频深度学习（EPSRC iCASE BAE）

基本信息

批准号：
1852482
负责人：
金额：
--
依托单位：
CARDIFF UNIVERSITY
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1852482
关键词：
Deep Learning Surveillance Video EPSRC

项目摘要

Deep Belief Networks are popular methods for a variety of pattern recognition/machine learning tasks often outperforming conventional computer analysis methods and humans. There is an emerging literature in static image analysis (e.g. http://image-net.org/about-publication). However, analysis of video is only recently beginning to be studied, the most prominent being the Facebook C3D work, which shows that posing the problem as a 3D (2D imagery over time) convolutional problem outperforms more traditional methods, and also augmented the raw pixel data with optical flow data. One issue that remains a fixed width window is used to capture and model the temporal dynamics. Whilst this might be appropriate for the tasks addressed in C3D (e.g. sports video classification) this may have limitations in many other domains. A recent paper at BMVC2015 also addresses the related area of anomaly detection in video.The project will build on recent investigation of how to detect fights in surveillance video, where activity occurs at varying duration and multi-scale (over time) have been developed to account for such features. Standard image feature recognition methods did not work well under challenge (night-time illumination, variable resolution, crowded scenes) and imaging conditions and texture based temporal features were developed to address the problem. It would interesting to compare a deep belief network approach.This project will also look more generally at detecting anomalous events, principally to develop alert systems assisting human operators to deal with the challenge of many hundreds of surveillance camera feeds in modern surveillance rooms. One extension would be to investigate methods to catalogue or summarise video - logging only important events or key frames (sequences) where events change or have been identified as anomalous or contain potential interesting activities.The main research challenge is to define how best to process temporal data of varying duration within a deep learning approach.

深度信念网络是用于各种模式识别/机器学习任务的流行方法，其性能往往优于传统的计算机分析方法和人类。在静态图像分析方面出现了一种新兴的文献(例如http://image-net.org/about-publication).然而，视频分析是最近才开始研究的，最突出的是Facebook的C3D工作，该工作表明，将问题描述为3D(随时间变化的2D图像)卷积问题的性能优于更传统的方法，并使用光流数据来增强原始像素数据。一个问题仍然是固定宽度的窗口，用于捕获时间动态并对其进行建模。虽然这可能适用于C3D中处理的任务(例如，体育视频分类)，但这在许多其他领域可能会有限制。最近在BMVC2015上的一篇论文也讨论了视频中异常检测的相关领域。该项目将建立在最近关于如何检测监控视频中的打斗的研究基础上，其中的活动发生在不同的持续时间，并且已经开发了多尺度(随时间)来考虑这些特征。标准的图像特征识别方法在挑战(夜间光照、可变分辨率、拥挤的场景)下不能很好地工作，成像条件和基于纹理的时间特征被开发出来解决这个问题。比较深度信念网络方法将是有趣的。这个项目还将更广泛地关注检测异常事件，主要是开发警报系统，帮助人类操作员应对现代监控室中数百个监控摄像头馈送的挑战。一个扩展将是研究将重要事件或关键帧(序列)进行视频记录的方法，其中事件发生变化或已被识别为异常或包含潜在的有趣活动。主要的研究挑战是定义如何在深度学习方法中最好地处理不同持续时间的时态数据。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Explaining Failure: Investigation of Surprise and Expectation in CNNs

DOI：
10.1109/cvprw50498.2020.00014
发表时间：
2020-06
期刊：
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
影响因子：
0
作者：
Thomas Hartley;K. Sidorov;C. Willis;David Marshall
通讯作者：
Thomas Hartley;K. Sidorov;C. Willis;David Marshall

SWAG: Superpixels Weighted by Average Gradients for Explanations of CNNs