People Identification Based on Person Image and Additional Physical Parameters Comparison

This paper proposes and presents one approach for people identification based on image and additional physical parameters, height and step length, of a person comparison. People identification is very important in many areas of human life. There are large number of identification methods (biometric methods) that include a different scope of methods, for example fingerprint identification, hand geometry identification, facial recognition, methods based on human eye identification (retina and iris), gait recognition etc. Most of that methods require some kind of interaction with a person, what could be a problem in many practical applications. The method that does not require any interaction with a person is gait recognition. One approach for a people identification based on gait recognition, that uses silhouettes of a person and parameters of person height and step length, is proposed and presented in this paper

Re-identification and semantic retrieval of pedestrians in video surveillance scenarios

Person re-identification consists of recognizing individuals across different sensors of a camera network. Whereas clothing appearance cues are widely used, other modalities could be exploited as additional information sources, like anthropometric measures and gait. In this work we investigate whether the re-identification accuracy of clothing appearance descriptors can be improved by fusing them with anthropometric measures extracted from depth data, using RGB-Dsensors, in unconstrained settings. We also propose a dissimilaritybased framework for building and fusing multi-modal descriptors of pedestrian images for re-identification tasks, as an alternative to the widely used score-level fusion. The experimental evaluation is carried out on two data sets including RGB-D data, one of which is a novel, publicly available data set that we acquired using Kinect sensors. In this dissertation we also consider a related task, named semantic retrieval of pedestrians in video surveillance scenarios, which consists of searching images of individuals using a textual description of clothing appearance as a query, given by a Boolean combination of predefined attributes. This can be useful in applications like forensic video analysis, where the query can be obtained froma eyewitness report. We propose a general method for implementing semantic retrieval as an extension of a given re-identification system that uses any multiple part-multiple component appearance descriptor. Additionally, we investigate on deep learning techniques to improve both the accuracy of attribute detectors and generalization capabilities. Finally, we experimentally evaluate our methods on several benchmark datasets originally built for re-identification task