ABSTRACT:In this work we investigate the problem of people recognition by their gait. For this task, we implement deep learning approach using the optical flow as the main source of motion information and combine neural feature extraction with the additional embedding of descriptors for representation improvement. In order to find the best heuristics, we compare several deep neural network architectures, learning and classification strategies. The experiments were made on two popular datasets for gait recognition, so we investigate their advantages and disadvantages and the transferability of considered methods.
Abstract. In this paper we propose the two-stage approach of organizing information in video surveillance systems. At first, the faces are detected in each frame and a video stream is split into sequences of frames with face region of one person. Secondly, these sequences (tracks) that contain identical faces are grouped using face verification algorithms and hierarchical agglomerative clustering. Gender and age are estimated for each cluster (person) in order to facilitate the usage of the organized video collection. The particular attention is focused on the aggregation of features extracted from each frame with the deep convolutional neural networks. The experimental results of the proposed approach using YTF and IJB-A datasets demonstrated that the most accurate and fast solution is achieved for matching of normalized average of feature vectors of all frames in a track.
In this paper we propose to organize information in video surveillance systems by grouping the video tracks, which contain identical faces. Aggregation of the features of individual frames extracted using deep convolutional neural networks are used in order to obtain a descriptor of video track. The tracks with identical faces are grouped using the known face verification algorithms and clustering methods. We experimentally compare frame aggregation methods using the YouTubeFaces dataset and contemporary neural networks (VGGFace, VGGFace2, LightenedCNN). It is shown that the most accurate video-based face verification is achieved with the L2-normalization of average unnormalized features of individual frames of each video track. Finally, we demonstrate that the best video grouping is obtained by sequential and rank-order clustering methods.
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