The percentage of false alarms caused by spiders in automated surveillance can range from 20-50%. False alarms increase the workload of surveillance personnel validating the alarms and the maintenance labor cost associated with regular cleaning of webs. We propose a novel, cost effective method to detect false alarms triggered by spiders/webs in surveillance camera networks. This is accomplished by building a spider classifier intended to be a part of the surveillance video processing pipeline. The proposed method uses a feature descriptor obtained by early fusion of blur and texture. The approach is sufficiently efficient for real-time processing and yet comparable in performance with more computationally costly approaches like SIFT with bag of visual words aggregation. The proposed method can eliminate 98.5% of false alarms caused by spiders in a data set supplied by an industry partner, with a false positive rate of less than 1%.

Test collections have a long history of supporting repeatable and comparable evaluation in Information Retrieval (IR). However, thus far, no shared test collection exists for IR systems that are designed to index and retrieve multimodal lifelog data. In this paper we introduce the first test collection for personal lifelog data, which has been employed for the NTCIR12-Lifelog task. In this paper, the requirements for the test collection are motivated, the process of creating the test collection is described, along with an overview of the test collection. Finally suggestions are given for possible applications of the test collection.

Wearable devices such as Google Glass are receiving increasing attention and look set to become part of our technical landscape over the next few years. At the same time, lifelogging is a topic that is growing in popularity with a host of new devices on the market that visually capture life experience in an automated manner. In this paper, we describe a visual lifelogging solution for Google Glass that is designed to capture life experience in rich visual detail, yet maintain the privacy of unknown bystanders.

Smart-phones are becoming our constant companions, they are with us all of the time, being used for calling, web surfing, apps, music listening, TV viewing, social networking, buying, gaming, and a myriad of other uses. Smart-phones are a technology that knows us much better than most of us could imagine. Based on our usage and the fact that we are never far away from our smart phones, they know where we go, who we interact with, what information we consume, and with a little clever software, they can know what we are doing and even why we are doing it. They are beginning to know us better than we know ourselves. In this work we present SenseSeer a generic mobile-cloud-based mobile Lifelogging framework. This framework supports customisable analytic services for sensing the person, understanding the semantics of life activities and the easy deployment of analytic tools and novel interfaces. At present, SenseSeer supports services in many domains, such as personal health monitoring, location tracking, lifestyle analysis and tourism focused applications. This work demonstrate the design principles of SenseSeer and three of its services: My Health, My Location and My Social Activity.

Regions Of Interest (ROI) are successfully used in automatic image annotation through Bag of Visual Words (BoVW) models. However, the "flat" BoVW models ignore potentially relevant topological relationships between the ROI. While several works tried to take into account such relations for Content Based Image Retrieval (CBIR), few works evaluated clearly their effects in the context of automatic image annotation. In this article, we propose to explicit the relationships between ROI in automatic annotation methods in order to evaluate their contributions on the results quality. This is done through a model, called "phrasing model", that generates groups of ROI called Visual Phrases according to relationships constraints. Based on our phrasing model, we define two kinds of Visual Phrases and we show that taking into account topological ROI relationships can significantly overcome the results of the classical BoVW model.