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In this paper, we present a method for human action recognition using local joints structure and histograms of 3D joints. Global features like histograms of 3D joints [12] ignore the local structure information of the human body joints, which is also essential for accurate action recognition. To address this problem, we propose a local joints structure feature as a complement, and combine both global...
In this paper we propose an efficient method for behavior recognition and identification of anomalous behavior in video surveillance data. This approach consists of two phases of training and testing. In the training phase, first, we use background subtraction method to extract the moving pixels. Then optical flow vectors are extracted for moving pixels. We propose behavior features of each pixel...
This paper proposes a system to recognize isolated American Sign Language and numbers in real-time from Bumblebee stereo camera using Latent-Dynamic Conditional Random Fields (LDCRFs). Our system is based on three main stages: preprocessing, feature extraction and classification. In preprocessing stage, color and 3D depth map are used to detect and track the hand. The second stage, combining features...
Automatic semantic annotation of video events has received a large attention from the scientific community in the latest years. Events can be defined by spatio-temporal relations and properties of objects and entities, which change over time; some events can be described by a set of patterns. Despite this application of dynamic graphical modeling, the performance for event modeling and detection continues...
This paper presents a novel method for identity recognition based on the 2D gait representation: Gait Energy Image (GEI) which is the averaged silhouette over one gait cycle. An ensemble of Gabor kernels is first convolved with GEI to extract discriminative feature. The obtained Gabor gait representation is then projected into lower dimensional subspace using discriminative common vectors (DCV) analysis...
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