Early Recognition of Human Activities from First-Person Videos Using Onset Representations

In this paper, we propose a methodology for early recognition of human activities from videos taken with a first-person viewpoint. Early recognition, which is also known as activity prediction, is an ability to infer an ongoing activity at its early stage. We present an algorithm to perform recognition of activities targeted at the camera from streaming videos, making the system to predict intended activities of the interacting person and avoid harmful events before they actually happen. We introduce the novel concept of 'onset' that efficiently summarizes pre-activity observations, and design an approach to consider event history in addition to ongoing video observation for early first-person recognition of activities. We propose to represent onset using cascade histograms of time series gradients, and we describe a novel algorithmic setup to take advantage of onset for early recognition of activities. The experimental results clearly illustrate that the proposed concept of onset enables better/earlier recognition of human activities from first-person videos

Ultrahigh energy neutrino scattering onto relic light neutrinos in galactic halo as a possible source of highest energy extragalactic cosmic rays

The diffuse relic neutrinos with light mass are transparent to Ultrahigh energy (UHE) neutrinos at thousands EeV, born by photoproduction of pions by UHE protons on relic 2.73 K BBR radiation and originated in AGNs at cosmic distances. However these UHE $\nu$s may interact with those (mainly heaviest $\nu_{\mu_r}$, $\nu_{\tau_r}$ and respective antineutrinos) clustered into HDM galactic halos. UHE photons or protons, secondaries of $\nu\nu_r$ scattering, might be the final observed signature of such high-energy chain reactions and may be responsible of the highest extragalactic cosmic-ray (CR) events. The chain-reactions conversion efficiency, ramifications and energetics are considered for the October 1991 CR event at 320 EeV observed by the Fly's Eye detector in Utah. These quantities seem compatible with the distance, direction and power (observed at MeV gamma energies) of the Seyfert galaxy MCG 8-11-11. The $\nu\nu_r$ interaction probability is favoured by at least three order of magnitude with respect to a direct $\nu$ scattering onto the Earth atmosphere. Therefore, it may better explain the extragalactic origin of the puzzling 320 EeV event, while offering indirect evidence of a hot dark galactic halo of light (i.e., $m_\nu\sim$ tens eV) neutrinos, probably of tau flavour.Comment: 25 pages, 1 figure minor corrections, updated references. In press in AP

Online Action Detection

In online action detection, the goal is to detect the start of an action in a video stream as soon as it happens. For instance, if a child is chasing a ball, an autonomous car should recognize what is going on and respond immediately. This is a very challenging problem for four reasons. First, only partial actions are observed. Second, there is a large variability in negative data. Third, the start of the action is unknown, so it is unclear over what time window the information should be integrated. Finally, in real world data, large within-class variability exists. This problem has been addressed before, but only to some extent. Our contributions to online action detection are threefold. First, we introduce a realistic dataset composed of 27 episodes from 6 popular TV series. The dataset spans over 16 hours of footage annotated with 30 action classes, totaling 6,231 action instances. Second, we analyze and compare various baseline methods, showing this is a challenging problem for which none of the methods provides a good solution. Third, we analyze the change in performance when there is a variation in viewpoint, occlusion, truncation, etc. We introduce an evaluation protocol for fair comparison. The dataset, the baselines and the models will all be made publicly available to encourage (much needed) further research on online action detection on realistic data.Comment: Project page: http://homes.esat.kuleuven.be/~rdegeest/OnlineActionDetection.htm