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We present a novel methodology that combines stereo vision and parallel processing, based on GPU and the use of binary descriptors, for fast plane extraction. Typical stereo algorithms require an image rectification stage that has to run in a frame-to-frame basis, increasing the computational burden and with the possibility of compromising high frame rate operation. Hence, we propose to use a semi-calibrated...
Scene depth variation is an important factor that leads to spatially-varying camera motion blur. Most of the previous methods require auxiliary cameras or user interaction to make depth-aware deblurring tractable. In this work, we propose to use a noisy/blurred/noisy image sequence and simultaneously recorded inertial measurements to jointly estimate scene depth and remove spatially-varying blur caused...
In this paper, we propose a new Multi-kernel Metric Learning (MKML) approach to enhance the performance of person re-identification using adaptive weighted Multi-kernel. The intuition behind our approach is that different features, i.e., low-level and middle-level features, have different nature and thus discriminating capability, utilizing different kernels could map these features into sub-spaces,...
Power line inspection is an essential but costly task while automated UAV (unmanned aerial vehicle) inspection can greatly reduce such costs. However, navigating along power lines is a challenging task due to the narrow width and limited features of power lines. Existing power line tracking methods have threshold selection problems and cannot work well for complex and changing backgrounds. We make...
Domain adaptation (DA) algorithms address the problem of distribution shift between training and testing data. Recent approaches transform data into a shared subspace by minimizing the shift between their marginal distributions. We propose a method to learn a common subspace that will leverage the class conditional distributions of training samples along with reducing the marginal distribution shift...
In this work we explore the previously proposed approach of direct blind deconvolution and denoising with convolutional neural networks (CNN) in a situation where the blur kernels are partially constrained. We focus on blurred images from a real-life traffic surveillance system, on which we, for the first time, demonstrate that neural networks trained on artificial data provide superior reconstruction...
Most of the existing works on person re-identification have focused on improving matching rate at top ranks. Few efforts are devoted to address the problem of efficient storage and fast search for person re-identification. In this paper, we investigate the prevailing hashing method, originally designed for large scale image retrieval, for fast person re-identification with efficient storage. We propose...
In this paper we propose a video aesthetic quality assessment method that combines the representation of each video according to a set of photographic and cinematographic rules, with the use of a learning method that takes the video representation's uncertainty into consideration. Specifically, our method exploits the information derived from both low- and high-level analysis of video layout, leading...
We propose a passive forgery detection technique for locating spliced regions in motion blurred images of 3D scenes. We consider general camera motion in hand-held cameras and utilize discrepancies in local motion blur patterns as a cue for splicing detection. We first devise an automatic and computationally efficient scheme to estimate the camera motion using only the blur kernels from authentic...
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Most existing person re-identification (ReID) methods assume the availability of extensively labelled cross-view person pairs and a closed-set scenario (i.e. all the probe people exist in the gallery set). These two assumptions significantly limit their usefulness and scalability in real-world applications, particularly with large scale camera networks. To overcome the limitations, we introduce a...
Aiming at the problem that the action recognition algorithms based on vision have a high requirement of the background and the human position relative to the sensor, an algorithm which is robust to the position changing of the human is proposed. The Microsoft v2 is used to collect skeleton data and standardize it, then the feature vectors are extracted from the data, at last after the correction of...
We propose an approach to indoor occupant localization using a network of single-pixel, visible-light sensors. In addition to preserving privacy, our approach vastly reduces data transmission rate and is agnostic to eavesdropping. We develop two purely data-driven localization algorithms and study their performance using a network of 6 such sensors. In one algorithm, we divide the monitored floor...
Airborne cameras on low-flying unmanned vehicles introduce new privacy challenges due to their mobility and viewing angles. In this paper, we focus on face recognition from airborne cameras and explore the design space to determine when a face in an airborne image is inherently protected, that is when an individual is not recognizable. Moreover, when individuals are recognizable by facial recognition...
Person re-identification (Re-ID) keeps the same identity for a person as he moves along an area with nonoverlapping surveillance cameras. Re-ID is a challenging task due to appearance changes caused by different camera viewpoints, occlusion and illumination conditions. While robust and discriminative descriptors are obtained combining texture, shape and color features in a high-dimensional representation,...
Many features have been proposed for person re-identification, but most of them are the combination of several different kinds of single features. And there is no research about what role the single features play and which can be fused together to improve the performance. In this paper, we explore eight single features (four colors, two textures, two gradients) and their fusions. Evaluations are conducted...
To improve the accuracy of corner's detection in the traditional black and white chessboard, a new method based on multi-features is proposed. Three distinct local features of the corners have been analyzed, they are structural response, symmetric response and edge response. By selectively applying these features, initial selection and later screening of potential corners have been done. Non-maximum...
Wide Area Motion Imagery (WAMI) are usually taken from unmaned air vehicles at low frame rates, and having very wide ground coverage. These images serve as rich source for many applications like surveillance, urban planing and traffic monitoring. Thus, understanding WAMI imagery exploitation has been gaining more interest recent years. In this paper, we focus on estimating the pose of vehicles in...
In this paper, we propose combined visual features for person re-identification. Our features are based on the multiple hand-crafted visual features. The proposed features are a combination of histogram from the RGB, YUV and HSV color channels, LBP and SIFT features. Then we use different distance metric learning methods to measure the similarity of the same persons and different persons. Experimental...
Achieving precise and robust human detection and tracking over camera networks is a very challenging task in the research of intelligent video surveillance. Its difficulties mainly result from abrupt human object motion, object occlusion and object scale change, and changing object appearance due to changes in illumination and viewpoint, non-rigid deformations, intra-class variability in shape and...
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