High-Level Descriptors for Fall Event Detection Supported by a Multi-Stream Network

The need for assertive video classification has been increasingly in demand. Especially for detecting endangering situations, it is crucial to have a quick response to avoid triggering more serious problems. During this work, we target video classification concerning falls. Our study focuses on the use of high-level descriptors able to correctly characterize the event. These descriptor results will serve as inputs to a multi-stream architecture of VGG-16 networks. Therefore, our proposal is based on the analysis of the best combination of high-level extracted features for the binary classification of videos. This approach was tested on three known datasets, and has proven to yield similar results as other more consuming methods found in the literature

A hierarchical image segmentation algorithm based on an observation scale

International audienceHierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Most image segmentation algorithms, such as region merging algorithms, rely on a criterion for merging that does not lead to a hierarchy. In addition, for image segmentation, the tuning of the parameters can be difficult. In this work, we propose a hierarchical graph based image segmentation relying on a criterion popularized by Felzenszwalb and Huttenlocher. Quantitative and qualitative assessments of the method on Berkeley image database shows efficiency, ease of use and robustness of our method

Analysis of Using Metric Access Methods for Visual Search of Objects in Video Databases

This article presents an approach to object retrieval that searches for and localizes all the occurrences of an object in a video database, given a query image of the object. Our proposal is based on text-retrieval methods in which video key frames are represented by a dense set of viewpoint invariant region descriptors that enable recognition to proceed successfully despite changes in camera viewpoint, lighting, and partial occlusions. Vector quantizing these region descriptors provides a visual analogy of a word - a visual word. Those words are grouped into a visual vocabulary which is used to index all key frames from the video database. Efficient retrieval is then achieved by employing methods from statistical text retrieval, including inverted file systems, and text-document frequency weightings. Though works in the literature have only adopted a simple sequential scan during search, we investigate the use of different metric access methods (MAM): M-tree, Slim-tree, and D-index, in order to accelerate the processing of similarity queries. In addition, a ranking strategy based on the spatial layout of the regions (spatial consistency) is fully described and evaluated. Experimental results have shown that the adoption of MAMs not only has improved the search performance but also has reduced the influence of the vocabulary size over test results, which may improve the scalability of our proposal. Finally, the application of spatial consistency has produced a very significant improvement of the results

Video fade detection by discrete line identification

The video segmentation problem can be regarded as a problem of detecting the fundamental video units (shots). Due to different ways of linking two consecutive shots this task turns out to be difficult. In this work, we propose a method to detect a type of gradual transition, the fade, by image segmentation tools instead of using dissimilarity measures or mathematical models. Firstly, the video is transformed into a 2D image considering the histogram information, called visual rhythm by histogram. Afterwards, we apply image processing tools to detect specified patterns in this image.

Removing non-signicant regions in hierarchical clustering and segmentation

International audienceWe propose an efficient algorithm that removes unimportant regions from a hierarchical partition tree, while preserving the hierarchical partition structure. Various experiments demonstrate that applying this algorithm on various classification or segmentation problems does indeed improve the results by a large margin. Code is available online at https://github.com/higra/Higra

Swarm Check-ins

<p>This database was created and collected with the goal of observing patterns in urban mobility. To achieve this, the Twitter API was used to collect public check-ins made by users via Foursquare Swarm. </p><p>Data description:</p><ul><li><strong> venueID:</strong> Foursquare venue identifier  </li><li><strong> userID:</strong> Swarm user identifier</li><li> <strong>venueName:</strong> Name of the venue where the check-in was made  </li><li> <strong>category:</strong> Category of the venue where the check-in was made  </li><li> <strong>country:</strong> Country of the venue where the check-in was made</li><li> <strong>city:</strong> City of the venue where the check-in was made</li><li> <strong>timestamp:</strong> Time when the check-in was shared on Twitter</li><li> <strong>latitude:</strong> Latitude of the venue where the check-in was made</li><li> <strong>longitude:</strong> Longitude of the venue where the check-in was made</li></ul><p> </p><p> </p><p> </p&gt