LOCATE: LOcation adaptive Constrained Activity recognition using Transfer learning

据估计，英国有600万个监控摄像头，其中只有17%是公共运营的。越来越多的人在家里安装闭路电视摄像头，用于安全或远程监控老人、婴儿或宠物。尽管这一数字有所增加，但绝大多数此类摄像头的使用仅限于取证或现场观看。这些传感器目前无法提供智能监测-识别处于危险中的婴儿或脱水的老年人。同样，公共场所的闭路电视大多用于取证。经过多年的研究，现在已经有了能够自动识别感兴趣的活动的方法，比如一个人在没有支付加油费用的情况下离开加油站，或者一个人篡改了加油机，取得了可以接受的成功率和较低的错误警报。虽然安装后是自动的，但安装过程不仅需要将硬件安装到位，还需要专家研究录像并设计适合监控地点的模型。在每个新的地点，例如每个新的服务站，都需要一种新的模式，这需要专家的努力和时间。这是昂贵的、难以扩展的，有时甚至是不可信的，例如用于家庭监控。该解决方案的灵感来自于人类智能地应用先前获得的知识来解决新挑战的能力。研究人员将与来自英国领先的视频分析行业合作伙伴QinetiQ和Thales的资深科学家合作。利用这些合作伙伴的专业知识，该项目将提供实用和有价值的见解，进一步推动强大的英国视频分析行业。英国目前是视频分析市场的全球参与者，也是欧洲、中东和非洲(EMEA)地区的领先国家。该方法将适用于各种领域，包括家庭监控和公共场所的闭路电视。为了评估拟议的家庭监控方法，Locate将与EPSRC资助的项目SPERE一起工作，该项目旨在为布里斯托尔及其周围的住宅医疗保健开发和部署一个基于传感器的平台。Locate的调查结果将被整合到SPHERE平台中，以实现对新家日常生活活动的自动监测，如做饭、吃饭或服药。例如，这种有针对性的即插即用方法将使非专家用户能够设置摄像头，并自动检测家中的老人是否吃过饭和吃过药。店主同样可以在他们的商店里侦测到扒窃企图。因此，社区可以更好地利用已经到位的视觉传感器网络。

It is estimated that there are six million surveillance cameras in the UK, with only 17% of them publicly operated. Increasingly, people are installing CCTV cameras in their homes for security or remote monitoring of elderly, infants or pets. Despite this increase, the use of the overwhelming majority of these cameras is limited to evidence gathering or live viewing. These sensors are currently incapable of providing smart monitoring - identifying an infant in danger or a dehydrated elderly. Similarly, CCTV in public places is mostly used for evidence gathering. Following years of research, methods capable of automatically recognising activities of interest, such as a person departing a service station without making a payment for refueling the car, or one tampering with a fuel dispenser, are now available, achieving acceptable levels of success and low false alarms. Though automatic after installation, the installation process not only requires putting the hardware in place but also involves an expert studying the footage and designing a model suitable for the monitored location. At each new location, e.g. each new service station, a new model is needed, requiring the effort and time of an expert. This is expensive, difficult to scale and at times implausible such as for home monitoring for example. This requirement to build location-specific models is currently limiting the adoption of automatic recognition of activities, despite the potential benefits.This project, LOCATE, proposes an algorithmic solution that is capable of using a pre-built model in a different location and adapting it by simply observing the new scene for a few days. The solution is inspired by the human ability to intelligently apply previously-acquired knowledge to solve new challenges. The researchers will work with senior scientists from two leading UK video analytics industrial partners; QinetiQ and Thales. Using these partners' expertise, the project will provide practical and valuable insight that can further boost the strong UK industry of video analytics. The United Kingdom is currently a global player in the video analytics market, and the leading country in the Europe, Middle East and Africa (EMEA) region. The method will be applicable to various domains, including for home monitoring and CCTV in public places. To evaluate the proposed approach for home monitoring, LOCATE will work alongside the EPSRC-funded project SPHERE, which aims to develop and deploy a sensor-based platform for residential healthcare in and around Bristol. The findings of LOCATE will be integrated within the SPHERE platform, towards automatic monitoring of activities of daily living in a new home, such as preparing a meal, eating or taking medication. The targeted plug-and-play approach will enable a non-expert user to setup a camera and automatically detect whether an elderly in the home had had their meal and medication, for example. A shop owner can similarly detect pickpocketing attempts in their store. The community can thus make better use of the already in place network of visual sensors.