Vision-based Real-Time Aerial Object Localization and Tracking for UAV Sensing System

The paper focuses on the problem of vision-based obstacle detection and tracking for unmanned aerial vehicle navigation. A real-time object localization and tracking strategy from monocular image sequences is developed by effectively integrating the object detection and tracking into a dynamic Kalman model. At the detection stage, the object of interest is automatically detected and localized from a saliency map computed via the image background connectivity cue at each frame; at the tracking stage, a Kalman filter is employed to provide a coarse prediction of the object state, which is further refined via a local detector incorporating the saliency map and the temporal information between two consecutive frames. Compared to existing methods, the proposed approach does not require any manual initialization for tracking, runs much faster than the state-of-the-art trackers of its kind, and achieves competitive tracking performance on a large number of image sequences. Extensive experiments demonstrate the effectiveness and superior performance of the proposed approach.Comment: 8 pages, 7 figure

BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning

Understanding the global optimality in deep learning (DL) has been attracting more and more attention recently. Conventional DL solvers, however, have not been developed intentionally to seek for such global optimality. In this paper we propose a novel approximation algorithm, BPGrad, towards optimizing deep models globally via branch and pruning. Our BPGrad algorithm is based on the assumption of Lipschitz continuity in DL, and as a result it can adaptively determine the step size for current gradient given the history of previous updates, wherein theoretically no smaller steps can achieve the global optimality. We prove that, by repeating such branch-and-pruning procedure, we can locate the global optimality within finite iterations. Empirically an efficient solver based on BPGrad for DL is proposed as well, and it outperforms conventional DL solvers such as Adagrad, Adadelta, RMSProp, and Adam in the tasks of object recognition, detection, and segmentation

SiO Maser Survey towards off-plane O-rich AGBs around the orbital plane of the Sagittarius Stellar Stream

We conducted an SiO maser survey towards 221 O-rich AGB stars with the aim of identifying maser emission associated with the Sagittarius stellar stream. In this survey, maser emission was detected in 44 targets, of which 35 were new detections. All of these masers are within 5 kpc of the Sun. We also compiled a Galactic SiO maser catalogue including ~2300 SiO masers from the literature. The distribution of these SiO masers give a scale height of 0.40 kpc, while 42 sources deviate from the Galactic plane by more than 1.2 kpc, half of which were found in this survey. Regarding SiO masers in the disc, we found both the rotational speeds and the velocity dispersions vary with the Galactic plane distance. Assuming Galactic rotational speed $\Theta$0 = 240 km/s , we derived the velocity lags are 15 km/s and 55 km/s for disc and off-plane SiO masers respectively. Moreover, we identified three groups with significant peculiar motions (with 70% confidence). The most significant group is in the thick disc that might trace stream/peculiar motion of the Perseus arm. The other two groups are mainly made up of off-plane sources. The northern and southern off-plane sources were found to be moving at ~33 km/s and 54 km/s away from the Galactic plane, respectively. Causes of these peculiar motions are still unclear. For the two off-plane groups, we suspect they are thick disc stars whose kinematics affected by the Sgr stellar stream or very old Sgr stream debris.Comment: 27 pages, 17 figures, MNRAS accepted 2017 September 1

Unsupervised Deep Feature Transfer for Low Resolution Image Classification

In this paper, we propose a simple while effective unsupervised deep feature transfer algorithm for low resolution image classification. No fine-tuning on convenet filters is required in our method. We use pre-trained convenet to extract features for both high- and low-resolution images, and then feed them into a two-layer feature transfer network for knowledge transfer. A SVM classifier is learned directly using these transferred low resolution features. Our network can be embedded into the state-of-the-art deep neural networks as a plug-in feature enhancement module. It preserves data structures in feature space for high resolution images, and transfers the distinguishing features from a well-structured source domain (high resolution features space) to a not well-organized target domain (low resolution features space). Extensive experiments on VOC2007 test set show that the proposed method achieves significant improvements over the baseline of using feature extraction.Comment: 4 pages, accepted to ICCV19 Workshop and Challenge on Real-World Recognition from Low-Quality Images and Video

Optimization for Training Deep Models and Deep Learning Based Point Cloud Analysis and Image Classification

Deep learning (DL) has dramatically improved the state-of-the-art performances in broad applications of computer vision, such as image recognition, object detection, segmentation, and point cloud analysis. However, the reasons for such huge empirical success of DL still keep elusive theoretically. In this dissertation, to understand DL and improve its efficiency, robustness, and interpretability, we theoretically investigate optimization algorithms for training deep models and empirically explore deep learning based point cloud analysis and image classification. 1). Optimization for Training Deep Models: Neural network training is one of the most difficult optimization problems involved in DL. Recently, it has been attracting more and more attention to understand the global optimality in DL. However, we observe that conventional DL solvers have not been developed intentionally to seek for such global optimality. In this dissertation, we propose a novel approximation algorithm, BPGrad, towards optimizing deep models globally via branch and pruning. The proposed BPGrad algorithm is based on the assumption of Lipschitz continuity in DL, and as a result, it can adaptively determine the step size for the current gradient given the history of previous updates, wherein theoretically no smaller steps can achieve the global optimality. Empirically an efficient solver based on BPGrad for DL is proposed as well, and it outperforms conventional DL solvers such as Adagrad, Adadelta, RMSProp, and Adam in the tasks of object recognition, detection, and segmentation. 2). Deep Learning Based Point Cloud Analysis and Image Classification: The network architecture is of central importance for many visual recognition tasks. In this dissertation, we focus on the emerging field of point clouds analysis and image classification. 2.1) Point cloud analysis: We observe that traditional 6D pose estimation approaches are not sufficient to address the problem where neither a CAD model of the object nor the ground-truth 6D poses of its instances are available during training. We propose a novel unsupervised approach to jointly learn the 3D object model and estimate the 6D poses of multiple instances of the same object in a single end-to-end deep neural network framework, with applications to depth-based instance segmentation. The inputs are depth images, and the learned object model is represented by a 3D point cloud. Specifically, our network produces a 3D object model and a list of rigid transformations on this model to generate instances, which when rendered must match the observed point cloud to minimizing the Chamfer distance. To render the set of instance point clouds with occlusions, the network automatically removes the occluded points in a given camera view. Extensive experiments evaluate our technique on several object models and a varying number of instances in 3D point clouds. Compared with popular baselines for instance segmentation, our model not only demonstrates competitive performance, but also learns a 3D object model that is represented as a 3D point cloud. 2.2) Low quality image classification: We propose a simple while effective unsupervised deep feature transfer network to address the degrading problem of the state-of-the-art classification algorithms on low-quality images. No fine-tuning is required in our method. We use a pre-trained deep model to extract features for both high-resolution (HR) and low-resolution (LR) images, and feed them into a multilayer feature transfer network for knowledge transfer. An SVM classifier is learned directly using these transferred low-resolution features. Our network can be embedded into the state-of-the-art network models as a plug-in feature enhancement module. It preserves data structures in feature space for HR images, and transfers the distinguishing features from a well-structured source domain (HR features space) to a not well-organized target domain (LR features space). Extensive experiments show that the proposed transfer network achieves significant improvements over the baseline method