PEOPLE REIDENTIFICATION IN A DISTRIBUTED CAMERA NETWORK

This paper presents an approach to the people reidentification problem in a distributed camera network system. The reidentification or reacquisition problem consists essentially on the matching process of images acquired from different cameras. This work is applied in a monitored environment by cameras. This application is important to modern security systems, in which the targets presence identication in the environment expands the capacity of action by security agents in real time and provides important parameters like localization for each target. We used the target\u27s color and target\u27s interest points with features for reidentication. The satisfactory results were obtained from real experiments in public video datasets and synthetic images with noise

Deep Learning for Person Reidentification Using Support Vector Machines

© 2017 Mengyu Xu et al. Due to the variations of viewpoint, pose, and illumination, a given individual may appear considerably different across different camera views. Tracking individuals across camera networks with no overlapping fields is still a challenging problem. Previous works mainly focus on feature representation and metric learning individually which tend to have a suboptimal solution. To address this issue, in this work, we propose a novel framework to do the feature representation learning and metric learning jointly. Different from previous works, we represent the pairs of pedestrian images as new resized input and use linear Support Vector Machine to replace softmax activation function for similarity learning. Particularly, dropout and data augmentation techniques are also employed in this model to prevent the network from overfitting. Extensive experiments on two publically available datasets VIPeR and CUHK01 demonstrate the effectiveness of our proposed approach

Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks

Person re-identification is an open and challenging problem in computer vision. Existing approaches have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re-identification problem, where a new camera may be temporarily inserted into an existing system to get additional information. To address such a novel and very practical problem, we propose an unsupervised adaptation scheme for re-identification models in a dynamic camera network. First, we formulate a domain perceptive re-identification method based on geodesic flow kernel that can effectively find the best source camera (already installed) to adapt with a newly introduced target camera, without requiring a very expensive training phase. Second, we introduce a transitive inference algorithm for re-identification that can exploit the information from best source camera to improve the accuracy across other camera pairs in a network of multiple cameras. Extensive experiments on four benchmark datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art unsupervised learning based alternatives whilst being extremely efficient to compute.Comment: CVPR 2017 Spotligh

Person Re-Identification by Deep Joint Learning of Multi-Loss Classification

Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation alone. This ignores their joint benefit and mutual complementary effects. In this work, we show the advantages of jointly learning local and global features in a Convolutional Neural Network (CNN) by aiming to discover correlated local and global features in different context. Specifically, we formulate a method for joint learning of local and global feature selection losses designed to optimise person re-id when using only generic matching metrics such as the L2 distance. We design a novel CNN architecture for Jointly Learning Multi-Loss (JLML) of local and global discriminative feature optimisation subject concurrently to the same re-id labelled information. Extensive comparative evaluations demonstrate the advantages of this new JLML model for person re-id over a wide range of state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03, Market-1501).Comment: Accepted by IJCAI 201

Deep-Facial Feature-Based Person Reidentification for Authentication in Surveillance Applications

Person reidentification (Re-ID) has been a problem recently faced in computer vision. Most of the existing methods focus on body features which are captured in the scene with high-end surveillance system. However, it is unhelpful for authentication. The technology came up empty in surveillance scenario such as in London’s subway bomb blast, and Bangalore ATM brutal attack cases, even though the suspected images exist in official databases. Hence, the prime objective of this chapter is to develop an efficient facial feature-based person reidentification framework for controlled scenario to authenticate a person. Initially, faces are detected by faster region-based convolutional neural network (Faster R-CNN). Subsequently, landmark points are obtained using supervised descent method (SDM) algorithm, and the face is recognized, by the joint Bayesian model. Each image is given an ID in the training database. Based on their similarity with the query image, it is ranked with the Re-ID index. The proposed framework overcomes the challenges such as pose variations, low resolution, and partial occlusions (mask and goggles). The experimental results (accuracy) on benchmark dataset demonstrate the effectiveness of the proposed method which is inferred from the observation of receiver operating characteristic (ROC) curve and cumulative matching characteristics (CMC) curve