Commercial Flight Crew Decision-Making during Low-Visibility Approach Operations Using Fused Synthetic/Enhanced Vision Systems

NASA is investigating revolutionary crew-vehicle interface technologies that strive to proactively overcome aircraft safety barriers that would otherwise constrain the full realization of the next-generation air transportation system. A fixed-based piloted simulation experiment was conducted to evaluate the complementary use of Synthetic and Enhanced Vision technologies. Specific focus was placed on new techniques for integration and/or fusion of Enhanced and Synthetic Vision and its impact within a two-crew flight deck on the crew's decision-making process during low-visibility approach and landing operations. Overall, the experimental data showed that significant improvements in situation awareness, without concomitant increases in workload and display clutter, could be provided by the integration and/or fusion of synthetic and enhanced vision technologies for the pilot-flying and the pilot-not-flying. During non-normal operations, the ability of the crew to handle substantial navigational errors and runway incursions were neither improved nor adversely impacted by the display concepts. The addition of Enhanced Vision may not, unto itself, provide an improvement in runway incursion detection without being specifically tailored for this application. Existing enhanced vision system procedures were effectively used in the crew decision-making process during approach and missed approach operations but having to forcibly transition from an excellent FLIR image to natural vision by 100 ft above field level was awkward for the pilot-flying

Long-term Robust Tracking whith on Failure Recovery

This article aims at a new algorithm for tracking moving objects in the long term. We have tried to overcome some potential difficulties, first by a comparative study of the measuring methods of the difference and the similarity between the template and the source image. In the second part, an improvement of the best method allows us to follow the target in a robust way. This method also allows us to effectively overcome the problems of geometric deformation, partial occlusion and recovery after the target leaves the field of vision. The originality of our algorithm is based on a new model, which does not depend on a probabilistic process and does not require a data based detection in advance. Experimental results on several difficult video sequences have proven performance advantages over many recent trackers. The developed algorithm can be employed in several applications such as video surveillance, active vision or industrial visual servoing

Gaussian Processes with Context-Supported Priors for Active Object Localization

We devise an algorithm using a Bayesian optimization framework in conjunction with contextual visual data for the efficient localization of objects in still images. Recent research has demonstrated substantial progress in object localization and related tasks for computer vision. However, many current state-of-the-art object localization procedures still suffer from inaccuracy and inefficiency, in addition to failing to provide a principled and interpretable system amenable to high-level vision tasks. We address these issues with the current research. Our method encompasses an active search procedure that uses contextual data to generate initial bounding-box proposals for a target object. We train a convolutional neural network to approximate an offset distance from the target object. Next, we use a Gaussian Process to model this offset response signal over the search space of the target. We then employ a Bayesian active search for accurate localization of the target. In experiments, we compare our approach to a state-of-theart bounding-box regression method for a challenging pedestrian localization task. Our method exhibits a substantial improvement over this baseline regression method.Comment: 10 pages, 4 figure

Res2Net: A New Multi-scale Backbone Architecture

Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layer-wise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. The source code and trained models are available on https://mmcheng.net/res2net/.Comment: 11 pages, 7 figure

Refactoring, reengineering and evolution: paths to Geant4 uncertainty quantification and performance improvement

Ongoing investigations for the improvement of Geant4 accuracy and computational performance resulting by refactoring and reengineering parts of the code are discussed. Issues in refactoring that are specific to the domain of physics simulation are identified and their impact is elucidated. Preliminary quantitative results are reported.Comment: To be published in the Proc. CHEP (Computing in High Energy Physics) 201

The effect of experience and of dots\u2019 density and duration on the detection of coherent motion in dogs

Knowledge about the mechanisms underlying canine vision is far from being exhaustive, especially that concerning post- retinal elaboration. One aspect that has received little attention is motion perception, and in spite of the common belief that dogs are extremely apt at detecting moving stimuli, there is no scientific support for such an assumption. In fact, we recently showed that dogs have higher thresholds than humans for coherent motion detection (Kanizsar et al. in Sci Rep UK 7:11259, 2017). This term refers to the ability of the visual system to perceive several units moving in the same direction, as one coherently moving global unit. Coherent motion perception is commonly investigated using random dot displays, containing variable proportions of coherently moving dots. Here, we investigated the relative contribution of local and global integra- tion mechanisms for coherent motion perception, and changes in detection thresholds as a result of repeated exposure to the experimental stimuli. Dogs who had been involved in the previous study were given a conditioned discrimination task, in which we systematically manipulated dot density and duration and, eventually, re-assessed our subjects\u2019 threshold after extensive exposure to the stimuli. Decreasing dot duration impacted on dogs\u2019 accuracy in detecting coherent motion only at very low duration values, revealing the efficacy of local integration mechanisms. Density impacted on dogs\u2019 accuracy in a linear fashion, indicating less efficient global integration. There was limited evidence of improvement in the re-assessment but, with an average threshold at re-assessment of 29%, dogs\u2019 ability to detect coherent motion remains much poorer than that of humans

Lifting schemes for joint coding of stereoscopic pairs of satellite images

electronic version (5 pp.)International audienceStereo data compression is an important issue for the new generation of vision systems. In this paper, we are interested in lossless coding methods for stereo images allowing progressive reconstruction. Most of the existing approaches account for the mutual similarities between the left and the right images. More precisely, the disparity compensation process consists in predicting the right image from the left one based on the disparity map. Then, the disparity map, the reference image, and the residual image are encoded. In this work, we propose a novel approach based on the concept of vector lifting scheme. Its main feature is that it does not generate one residual image but two compact multiresolution representations of the left and the right views, driven by the underlying disparity map. Experimental results show a significant improvement using this technique compared with conventional methods

S-OHEM: Stratified Online Hard Example Mining for Object Detection

One of the major challenges in object detection is to propose detectors with highly accurate localization of objects. The online sampling of high-loss region proposals (hard examples) uses the multitask loss with equal weight settings across all loss types (e.g, classification and localization, rigid and non-rigid categories) and ignores the influence of different loss distributions throughout the training process, which we find essential to the training efficacy. In this paper, we present the Stratified Online Hard Example Mining (S-OHEM) algorithm for training higher efficiency and accuracy detectors. S-OHEM exploits OHEM with stratified sampling, a widely-adopted sampling technique, to choose the training examples according to this influence during hard example mining, and thus enhance the performance of object detectors. We show through systematic experiments that S-OHEM yields an average precision (AP) improvement of 0.5% on rigid categories of PASCAL VOC 2007 for both the IoU threshold of 0.6 and 0.7. For KITTI 2012, both results of the same metric are 1.6%. Regarding the mean average precision (mAP), a relative increase of 0.3% and 0.5% (1% and 0.5%) is observed for VOC07 (KITTI12) using the same set of IoU threshold. Also, S-OHEM is easy to integrate with existing region-based detectors and is capable of acting with post-recognition level regressors.Comment: 9 pages, 3 figures, accepted by CCCV 201