431 research outputs found
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
Efficient multi-view multi-target tracking using a distributed camera network
In this paper, we propose a multi-target tracking method using a distributed camera network, which can effectively handle the occlusion and reidenfication problems by combining advanced deep learning and distributed information fusion. The targets are first detected using a fast object detection method based on deep learning. We then combine the deep visual feature information and spatial trajectory information in the Hungarian algorithm for robust targets association. The deep visual feature information is extracted from a convolutional neural network, which is pre-trained using a large-scale person reidentification dataset. The spatial trajectories of multiple targets in our framework are derived from a multiple view information fusion method, which employs an information weighted consensus filter for fusion and tracking. In addition, we also propose an efficient track processing method for ID assignment using multiple view information. The experiments on public datasets show that the proposed method is robust to solve the occlusion problem and reidentification problem, and can achieve superior performance compared to the state of the art methods
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
Support Neighbor Loss for Person Re-Identification
Person re-identification (re-ID) has recently been tremendously boosted due
to the advancement of deep convolutional neural networks (CNN). The majority of
deep re-ID methods focus on designing new CNN architectures, while less
attention is paid on investigating the loss functions. Verification loss and
identification loss are two types of losses widely used to train various deep
re-ID models, both of which however have limitations. Verification loss guides
the networks to generate feature embeddings of which the intra-class variance
is decreased while the inter-class ones is enlarged. However, training networks
with verification loss tends to be of slow convergence and unstable performance
when the number of training samples is large. On the other hand, identification
loss has good separating and scalable property. But its neglect to explicitly
reduce the intra-class variance limits its performance on re-ID, because the
same person may have significant appearance disparity across different camera
views. To avoid the limitations of the two types of losses, we propose a new
loss, called support neighbor (SN) loss. Rather than being derived from data
sample pairs or triplets, SN loss is calculated based on the positive and
negative support neighbor sets of each anchor sample, which contain more
valuable contextual information and neighborhood structure that are beneficial
for more stable performance. To ensure scalability and separability, a
softmax-like function is formulated to push apart the positive and negative
support sets. To reduce intra-class variance, the distance between the anchor's
nearest positive neighbor and furthest positive sample is penalized.
Integrating SN loss on top of Resnet50, superior re-ID results to the
state-of-the-art ones are obtained on several widely used datasets.Comment: Accepted by ACM Multimedia (ACM MM) 201
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