Dictionary based action video classification with action bank

Classifying action videos became challenging problem in computer vision community. In this work, action videos are represented by dictionaries which are learned by online dictionary learning (ODL). Here, we have used two simple measures to classify action videos, reconstruction error and projection. Sparse approximation algorithm LASSO is used to reconstruct test video and reconstruction error is calculated for each of the dictionaries. To get another discriminative measure projection, the test vector is projected onto the atoms in the dictionary. Minimum reconstruction error and maximum projection give information regarding the action category of the test vector. With action bank as a feature vector, our best performance is 59.3% on UCF50 (benchmark is 57.9%), 97.7% on KTH (benchmark is 98.2%)and 23.63% on HMDB51 (benchmark is 26.9%)

Разработка средств сбора и логического анализа 3D-видеоданных на основе времяпролётной камеры и Акторного Пролога

Предложен подход к интеллектуальному 3D-видеонаблюдению на основе объектно-ориентированного логического программирования. В отличие от обычного 2D-видеонаблюдения, методы трёхмерного зрения обеспечивают надёжное распознавание частей тела, что делает возможным новые постановки задачи практическое применение методов анализа поведения людей в системах видеонаблюдения. Логический подход к интеллектуальному видеонаблюдению позволяет описывать сложное поведение людей на основе определений простых действий и поз. Цель данной работы заключается в реализации этих преимуществ логического подхода в области интеллектуального 3D-видеонаблюдения.Работа выполнена при поддержке РФФИ, грант № 16-29-09626-офи_м

Unusual events detection based on multi-dictionary sparse representation using kinect

Unusual events detection plays a crucial role in surveillance applications, which is becoming more and more urgent need for public security. However, illumination and scale changing, lacking of sufficient training data and subjective of abnormality definition are some of the severe difficulties, which are hard to deal with by widely used traditional cameras. In order to solve these problems, first, a novel feature is proposed in this paper, which is named random local feature (RLF) to describe the spatial-temporal information of depth image detected by the Kinect sensor. Then, we expand the sparse representation framework to a multi-dictionary sparse representation framework, based on the intuition that that anomaly of a same event may vary a lot in different regions in a scene. We split the depth video into several regions and use detected RLF features in each region to train dictionary by K-SVD algorithm, and use the OMP algorithm to sparse-represent each feature. Finally, an objective function is introduced to evaluate the anomaly of features in each region according to reconstruction errors. Unusual events are defined as those incidences that occur very rarely in the entire video sequence in our system, which is tested on real data and demonstrates promising results in unusual events detection.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000351597603011&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Imaging Science & Photographic TechnologyCPCI-S(ISTP)