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Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set
Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments
Unconstrained Face Verification using Deep CNN Features
In this paper, we present an algorithm for unconstrained face verification
based on deep convolutional features and evaluate it on the newly released
IARPA Janus Benchmark A (IJB-A) dataset. The IJB-A dataset includes real-world
unconstrained faces from 500 subjects with full pose and illumination
variations which are much harder than the traditional Labeled Face in the Wild
(LFW) and Youtube Face (YTF) datasets. The deep convolutional neural network
(DCNN) is trained using the CASIA-WebFace dataset. Extensive experiments on the
IJB-A dataset are provided
In-the-wild Facial Expression Recognition in Extreme Poses
In the computer research area, facial expression recognition is a hot
research problem. Recent years, the research has moved from the lab environment
to in-the-wild circumstances. It is challenging, especially under extreme
poses. But current expression detection systems are trying to avoid the pose
effects and gain the general applicable ability. In this work, we solve the
problem in the opposite approach. We consider the head poses and detect the
expressions within special head poses. Our work includes two parts: detect the
head pose and group it into one pre-defined head pose class; do facial
expression recognize within each pose class. Our experiments show that the
recognition results with pose class grouping are much better than that of
direct recognition without considering poses. We combine the hand-crafted
features, SIFT, LBP and geometric feature, with deep learning feature as the
representation of the expressions. The handcrafted features are added into the
deep learning framework along with the high level deep learning features. As a
comparison, we implement SVM and random forest to as the prediction models. To
train and test our methodology, we labeled the face dataset with 6 basic
expressions.Comment: Published on ICGIP201
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