Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps

Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks. In this paper, we propose a novel real-time hyperspectral likelihood maps-aided tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving object tracking system generally consists of registration, object detection, and tracking modules. We focus on the target detection part and remove the necessity to build any offline classifiers and tune a large amount of hyperparameters, instead learning a generative target model in an online manner for hyperspectral channels ranging from visible to infrared wavelengths. The key idea is that, our adaptive fusion method can combine likelihood maps from multiple bands of hyperspectral imagery into one single more distinctive representation increasing the margin between mean value of foreground and background pixels in the fused map. Experimental results show that the HLT not only outperforms all established fusion methods but is on par with the current state-of-the-art hyperspectral target tracking frameworks.Comment: Accepted at the International Conference on Computer Vision and Pattern Recognition Workshops, 201

Vision-based traffic surveys in urban environments

This paper presents a state-of-the-art, vision-based vehicle detection and type classification to perform traffic surveys from a roadside closed-circuit television camera. Vehicles are detected using background subtraction based on a Gaussian mixture model that can cope with vehicles that become stationary over a significant period of time. Vehicle silhouettes are described using a combination of shape and appearance features using an intensity-based pyramid histogram of orientation gradients (HOG). Classification is performed using a support vector machine, which is trained on a small set of hand-labeled silhouette exemplars. These exemplars are identified using a model-based preclassifier that utilizes calibrated images mapped by Google Earth to provide accurately surveyed scene geometry matched to visible image landmarks. Kalman filters track the vehicles to enable classification by majority voting over several consecutive frames. The system counts vehicles and separates them into four categories: car, van, bus, and motorcycle (including bicycles). Experiments with real-world data have been undertaken to evaluate system performance and vehicle detection rates of 96.45% and classification accuracy of 95.70% have been achieved on this data.The authors gratefully acknowledge the Royal Borough of Kingston for providing the video data. S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement nº 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander

BSUV-Net: a fully-convolutional neural network for background subtraction of unseen videos

Background subtraction is a basic task in computer vision and video processing often applied as a pre-processing step for object tracking, people recognition, etc. Recently, a number of successful background-subtraction algorithms have been proposed, however nearly all of the top-performing ones are supervised. Crucially, their success relies upon the availability of some annotated frames of the test video during training. Consequently, their performance on completely “unseen” videos is undocumented in the literature. In this work, we propose a new, supervised, background subtraction algorithm for unseen videos (BSUV-Net) based on a fully-convolutional neural network. The input to our network consists of the current frame and two background frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance of overfitting, we also introduce a new data-augmentation technique which mitigates the impact of illumination difference between the background frames and the current frame. On the CDNet-2014 dataset, BSUV-Net outperforms stateof-the-art algorithms evaluated on unseen videos in terms of several metrics including F-measure, recall and precision.Accepted manuscrip

Background Subtraction in Video Surveillance

The aim of thesis is the real-time detection of moving and unconstrained surveillance environments monitored with static cameras. This is achieved based on the results provided by background subtraction. For this task, Gaussian Mixture Models (GMMs) and Kernel density estimation (KDE) are used. A thorough review of state-of-the-art formulations for the use of GMMs and KDE in the task of background subtraction reveals some further development opportunities, which are tackled in a novel GMM-based approach incorporating a variance controlling scheme. The proposed approach method is for parametric and non-parametric and gives us the better method for background subtraction, with more accuracy and easier parametrization of the models, for different environments. It also converges to more accurate models of the scenes. The detection of moving objects is achieved by using the results of background subtraction. For the detection of new static objects, two background models, learning at different rates, are used. This allows for a multi-class pixel classification, which follows the temporality of the changes detected by means of background subtraction. In a first approach, the subtraction of background models is done for parametric model and their results are shown. The second approach is for non-parametric models, where background subtraction is done using KDE non-parametric model. Furthermore, we have done some video engineering, where the background subtraction algorithm was employed so that, the background from one video and the foreground from another video are merged to form a new video. By doing this way, we can also do more complex video engineering with multiple videos. Finally, the results provided by region analysis can be used to improve the quality of the background models, therefore, considerably improving the detection results

Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements

Background subtraction has been a fundamental and widely studied task in video analysis, with a wide range of applications in video surveillance, teleconferencing and 3D modeling. Recently, motivated by compressive imaging, background subtraction from compressive measurements (BSCM) is becoming an active research task in video surveillance. In this paper, we propose a novel tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames into backgrounds with spatial-temporal correlations and foregrounds with spatio-temporal continuity in a tensor framework. In this approach, we use 3D total variation (TV) to enhance the spatio-temporal continuity of foregrounds, and Tucker decomposition to model the spatio-temporal correlations of video background. Based on this idea, we design a basic tensor RPCA model over the video frames, dubbed as the holistic TenRPCA model (H-TenRPCA). To characterize the correlations among the groups of similar 3D patches of video background, we further design a patch-group-based tensor RPCA model (PG-TenRPCA) by joint tensor Tucker decompositions of 3D patch groups for modeling the video background. Efficient algorithms using alternating direction method of multipliers (ADMM) are developed to solve the proposed models. Extensive experiments on simulated and real-world videos demonstrate the superiority of the proposed approaches over the existing state-of-the-art approaches.Comment: To appear in IEEE TI