Stochastic Models for Multimodal Video Analysis

2008 ◽  
pp. 1-19
Author(s):  
Manolis Delakis ◽  
Guillaume Gravier ◽  
Patrick Gros
Keyword(s):  
Video Analysis ◽  
Stochastic Models

Searching for better layout of a shop--Agent-based approach based on video analysis

2008 ◽  
Author(s):  
Sato Hiroshi ◽  
Kubo Masao ◽  
Namatame Akira
Keyword(s):  
Video Analysis ◽  
Agent Based

Stochastic models for learning.

1955 ◽  
Vol 39 (6) ◽  
pp. 464-464
Author(s):  
John E. Anderson
Keyword(s):  
Stochastic Models

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

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