User-interface to a CCTV video search system

The proliferation of CCTV surveillance systems creates a problem of how to effectively navigate and search the resulting video archive, in a variety of security scenarios. We are concerned here with a situation where a searcher must locate all occurrences of a given person or object within a specified timeframe and with constraints on which camera(s) footage is valid to search. Conventional approaches based on browsing time/camera based combinations are inadequate. We advocate using automatically detected video objects as a basis for search, linking and browsing. In this paper we present a system under development based on users interacting with detected video objects. We outline the suite of technologies needed to achieve such a system and for each we describe where we are in terms of realizing those technologies. We also present a system interface to this system, designed with user needs and user tasks in mind

The Evolution of First Person Vision Methods: A Survey

The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart Glasses, Computer Vision, Video Analytics, Human-machine Interactio

Video Genre Classification Using Weighted Kernel Logistic Regression

Due to the widening semantic gap of videos, computational tools to classify these videos into different genre are highly needed to narrow it. Classifying videos accurately demands good representation of video data and an efficient and effective model to carry out the classification task. Kernel Logistic Regression (KLR), kernel version of logistic regression (LR), proves its efficiency as a classifier, which can naturally provide probabilities and extend to multiclass classification problems. In this paper, Weighted Kernel Logistic Regression (WKLR) algorithm is implemented for video genre classification to obtain significant accuracy, and it shows accurate and faster good results