Large scale visual-based event matching

International audienceOrganizing media according to real-life events is attracting interest in the multimedia community. Event-centric indexing approaches are very promising for discovering more complex relationships between data. In this paper we introduce a new visual-based method for retrieving events in photo collections, typically in the context of User Generated Contents. Given a query event record, represented by a set of photos, our method aims to retrieve other records of the same event, typically generated by distinct users. Similarly to what is done in state-of-the-art object retrieval systems, we propose a two-stage strategy combining an efficient visual indexing model with a spatiotemporal verification re-ranking stage to improve query performance. For efficiency and scalability concerns, we implemented the proposed method according to the MapReduce programming model using Multi-Probe Locality Sensitive Hashing. Experiments were conducted on LastFM-Flickr dataset for distinct scenarios, including event retrieval, automatic annotation and tags suggestion. As one result, our method is able to suggest the correct event tag over 5 suggestions with a 72% success rate

ESVIO: Event-based Stereo Visual Inertial Odometry

Event cameras that asynchronously output low-latency event streams provide great opportunities for state estimation under challenging situations. Despite event-based visual odometry having been extensively studied in recent years, most of them are based on monocular and few research on stereo event vision. In this paper, we present ESVIO, the first event-based stereo visual-inertial odometry, which leverages the complementary advantages of event streams, standard images and inertial measurements. Our proposed pipeline achieves temporal tracking and instantaneous matching between consecutive stereo event streams, thereby obtaining robust state estimation. In addition, the motion compensation method is designed to emphasize the edge of scenes by warping each event to reference moments with IMU and ESVIO back-end. We validate that both ESIO (purely event-based) and ESVIO (event with image-aided) have superior performance compared with other image-based and event-based baseline methods on public and self-collected datasets. Furthermore, we use our pipeline to perform onboard quadrotor flights under low-light environments. A real-world large-scale experiment is also conducted to demonstrate long-term effectiveness. We highlight that this work is a real-time, accurate system that is aimed at robust state estimation under challenging environments

DART: Distribution Aware Retinal Transform for Event-based Cameras

We introduce a generic visual descriptor, termed as distribution aware retinal transform (DART), that encodes the structural context using log-polar grids for event cameras. The DART descriptor is applied to four different problems, namely object classification, tracking, detection and feature matching: (1) The DART features are directly employed as local descriptors in a bag-of-features classification framework and testing is carried out on four standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS, NCaltech-101). (2) Extending the classification system, tracking is demonstrated using two key novelties: (i) For overcoming the low-sample problem for the one-shot learning of a binary classifier, statistical bootstrapping is leveraged with online learning; (ii) To achieve tracker robustness, the scale and rotation equivariance property of the DART descriptors is exploited for the one-shot learning. (3) To solve the long-term object tracking problem, an object detector is designed using the principle of cluster majority voting. The detection scheme is then combined with the tracker to result in a high intersection-over-union score with augmented ground truth annotations on the publicly available event camera dataset. (4) Finally, the event context encoded by DART greatly simplifies the feature correspondence problem, especially for spatio-temporal slices far apart in time, which has not been explicitly tackled in the event-based vision domain.Comment: 12 pages, revision submitted to TPAMI in Nov 201

Circulant temporal encoding for video retrieval and temporal alignment

We address the problem of specific video event retrieval. Given a query video of a specific event, e.g., a concert of Madonna, the goal is to retrieve other videos of the same event that temporally overlap with the query. Our approach encodes the frame descriptors of a video to jointly represent their appearance and temporal order. It exploits the properties of circulant matrices to efficiently compare the videos in the frequency domain. This offers a significant gain in complexity and accurately localizes the matching parts of videos. The descriptors can be compressed in the frequency domain with a product quantizer adapted to complex numbers. In this case, video retrieval is performed without decompressing the descriptors. We also consider the temporal alignment of a set of videos. We exploit the matching confidence and an estimate of the temporal offset computed for all pairs of videos by our retrieval approach. Our robust algorithm aligns the videos on a global timeline by maximizing the set of temporally consistent matches. The global temporal alignment enables synchronous playback of the videos of a given scene

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table