Outfit Recommender System

The online apparel retail market size in the United States is worth about seventy-two billion US dollars. Recommendation systems on retail websites generate a lot of this revenue. Thus, improving recommendation systems can increase their revenue. Traditional recommendations for clothes consisted of lexical methods. However, visual-based recommendations have gained popularity over the past few years. This involves processing a multitude of images using different image processing techniques. In order to handle such a vast quantity of images, deep neural networks have been used extensively. With the help of fast Graphics Processing Units, these networks provide results which are extremely accurate, within a small amount of time. However, there are still ways in which recommendations for clothes can be improved. We propose an event-based clothing recommendation system which uses object detection. We train a model to identify nine events/scenarios that a user might attend: White Wedding, Indian Wedding, Conference, Funeral, Red Carpet, Pool Party, Birthday, Graduation and Workout. We train another model to detect clothes out of fifty-three categories of clothes worn at the event. Object detection gives a mAP of 84.01. Nearest neighbors of the clothes detected are recommended to the user

Semi-Supervised First-Person Activity Recognition in Body-Worn Video

Body-worn cameras are now commonly used for logging daily life, sports, and law enforcement activities, creating a large volume of archived footage. This paper studies the problem of classifying frames of footage according to the activity of the camera-wearer with an emphasis on application to real-world police body-worn video. Real-world datasets pose a different set of challenges from existing egocentric vision datasets: the amount of footage of different activities is unbalanced, the data contains personally identifiable information, and in practice it is difficult to provide substantial training footage for a supervised approach. We address these challenges by extracting features based exclusively on motion information then segmenting the video footage using a semi-supervised classification algorithm. On publicly available datasets, our method achieves results comparable to, if not better than, supervised and/or deep learning methods using a fraction of the training data. It also shows promising results on real-world police body-worn video

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Seeing with sound? Exploring different characteristics of a visual-to-auditory sensory substitution device

Sensory substitution devices convert live visual images into auditory signals, for example with a web camera (to record the images), a computer (to perform the conversion) and headphones (to listen to the sounds). In a series of three experiments, the performance of one such device (‘The vOICe’) was assessed under various conditions on blindfolded sighted participants. The main task that we used involved identifying and locating objects placed on a table by holding a webcam (like a flashlight) or wearing it on the head (like a miner’s light). Identifying objects on a table was easier with a hand-held device, but locating the objects was easier with a head-mounted device. Brightness converted into loudness was less effective than the reverse contrast (dark being loud), suggesting that performance under these conditions (natural indoor lighting, novice users) is related more to the properties of the auditory signal (ie the amount of noise in it) than the cross-modal association between loudness and brightness. Individual differences in musical memory (detecting pitch changes in two sequences of notes) was related to the time taken to identify or recognise objects, but individual differences in self-reported vividness of visual imagery did not reliably predict performance across the experiments. In general, the results suggest that the auditory characteristics of the device may be more important for initial learning than visual associations

The Cyborg Astrobiologist: Testing a Novelty-Detection Algorithm on Two Mobile Exploration Systems at Rivas Vaciamadrid in Spain and at the Mars Desert Research Station in Utah

(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de Santiuste), and the phone-camera has been tested at a geological field site in Malta. In this work, we (i) apply a Hopfield neural-network algorithm for novelty detection based upon color, (ii) integrate a field-capable digital microscope on the wearable computer platform, (iii) test this novelty detection with the digital microscope at Rivas Vaciamadrid, (iv) develop a Bluetooth communication mode for the phone-camera platform, in order to allow access to a mobile processing computer at the field sites, and (v) test the novelty detection on the Bluetooth-enabled phone-camera connected to a netbook computer at the Mars Desert Research Station in Utah. This systems engineering and field testing have together allowed us to develop a real-time computer-vision system that is capable, for example, of identifying lichens as novel within a series of images acquired in semi-arid desert environments. We acquired sequences of images of geologic outcrops in Utah and Spain consisting of various rock types and colors to test this algorithm. The algorithm robustly recognized previously-observed units by their color, while requiring only a single image or a few images to learn colors as familiar, demonstrating its fast learning capability.Comment: 28 pages, 12 figures, accepted for publication in the International Journal of Astrobiolog

Non-overlapping dual camera fall detection using the NAO humanoid robot

With an aging population and a greater desire for independence, the dangers of falling incidents in the elderly have become particularly pronounced. In light of this, several technologies have been developed with the aim of preventing or monitoring falls. Failing to strike the balance between several factors including reliability, complexity and invasion of privacy has seen prohibitive in the uptake of these systems. Some systems rely on cameras being mounted in all rooms of a user's home while others require being worn 24 hours a day. This paper explores a system using a humanoid NAO robot with dual vertically mounted cameras to perform the task of fall detection

Mining user activity as a context source for search and retrieval

Nowadays in information retrieval it is generally accepted that if we can better understand the context of users then this could help the search process, either at indexing time by including more metadata or at retrieval time by better modelling the user context. In this work we explore how activity recognition from tri-axial accelerometers can be employed to model a user's activity as a means of enabling context-aware information retrieval. In this paper we discuss how we can gather user activity automatically as a context source from a wearable mobile device and we evaluate the accuracy of our proposed user activity recognition algorithm. Our technique can recognise four kinds of activities which can be used to model part of an individual's current context. We discuss promising experimental results, possible approaches to improve our algorithms, and the impact of this work in modelling user context toward enhanced search and retrieval