Limited by aircraft flight altitude and camera parameters, it is necessary to obtain wide-angle panoramas quickly by stitching aerial images, which is helpful in rapid disaster investigation, recovery after earthquakes, and aerial reconnaissance. However, most existing stitching algorithms do not simultaneously meet practical real-time, robustness, and accuracy requirements, especially in the case of a long-distance multistrip flight. In this paper, we propose a novel imageonly real-time UAV image mosaic framework for long-distance multistrip flights that does not require any auxiliary information, such as GPS or GCPs...Read More
Existing deep learning methods for action recognition in videos require a large number of labeled videos for training, which is labor-intensive and time-consuming. For the same action, the knowledge learned from different media types, e.g., videos and images, may be related and complementary. However, due to the domain shifts and heterogeneous feature representations between videos and images, the performance of classifiers trained on images may be dramatically degraded when directly deployed to videos. In this paper, we propose a novel method, named Deep Image-to-Video Adaptation and Fusion Networks (DIVAFN)...Read More
Tracking multiple people in crowds is a fundamental and essential task in the multimedia field. It is often hindered by difficulties such as dynamic occlusion between objects, cluttered background and abrupt illumination changes. To respond to this need, in this paper, we combine deep and depth to build a stereo tracking system for crowds. The core of the system is the fusion of the advantages of deep learning and depth information, which is exploited to achieve object segmentation and improve the multiobject tracking performance in severe occlusion...Read More
With the rapid development of unmanned aerial vehicles (UAVs), UAV-based intelligent airborne surveillance systems represented by real-time ground vehicle speed estimation have attracted wide attention from researchers. However, there are still many challenges in extracting speed information from UAV videos, including the dynamic moving background, small target size, complicated environment, and diverse scenes. In this paper, we propose a novel adaptive framework for multi-vehicle ground speed estimation in airborne videos. Firstly, we build a traffic dataset based on UAV...Read More
Moving target detection plays a primary and pivotal role in avionics visual analysis, which aims to completely and accurately detect moving objects from complex backgrounds. However, due to the relatively small sizes of targets in aerial video, many deep networks that achieve success in normal size object detection are usually accompanied by a high rate of false alarms and missed detections. To address this problem, we propose a novel visual detail augmented mapping approach for small aerial target detection. Concretely, we first present a multi-cue foreground segmentation algorithm including motion and grayscale information to extract potential regions...Read More
Synthetic aperture imaging, which has been proved to be an effective approach for occluded object imaging, is one of the challenging problems in the field of computational imaging. Currently most of the related researches focus on fixed synthetic aperture which usually accompanies with mixed observation angle and foreground de-focus blur. But the existence of them is frequently a source of perspective effect decrease and occluded object imaging quality degradation. In order to solve this problem, we propose a novel data-driven variable synthetic aperture imaging based on semantic feedback...Read More
In recent years, UAV technology has developed rapidly. Due to the mobility, low cost, and variable monitoring altitude of UAVs, multiple-object detection and tracking in aerial videos has become a research hotspot in the field of computer vision. However, due to camera motion, small target size, target adhesion, and unpredictable target motion, it is still difficult to detect and track targets of interest in aerial videos, especially in the case of a low frame rate where the target position changes too much. In this paper, we propose a multiple-object-tracking algorithm based on dense-trajectory voting in aerial videos...Read More
In recent years, unmanned aerial vehicles (UAVs) have rapidly developed, but the illegal use of UAVs by civilians has resulted in disorder and security risks and has increasingly triggered community concern and worry. Therefore, the monitoring and recycling of UAVs in key regions is of great significance. This paper presents a novel panoramic UAV surveillance and autonomous recycling system that is based on an unique structure-free fisheye camera array and has the capability of real-time UAV detection...Read More
Recognizing human actions from varied views is challenging due to huge appearance variations in different views. The key to this problem is to learn discriminant view-invariant representations generalizing well across views. In this paper, we address this problem by learning view-invariant representations hierarchically using a novel method, referred to as Joint Sparse Representation and Distribution Adaptation (JSRDA).. . .Read More
As the two most commonly used imaging devices, infrared sensor and visible sensor play a vital and essential role in the field of heterogeneous image matching. Therefore, visible-infrared image matching which aims to search images across them has important application and theoretical significance. However, due to the vastly different imaging principles, how to accurately match between visible and infrared image remains a challenge. . .Read More
Infrared human action recognition has many advantages, i.e., it is insensitive to illumination change, appearance variability, and shadows. Existing methods for infrared action recognition are either based on spatial or local temporal information, however, the global temporal information, which can better describe the movements of body parts across the whole video. . .Read More
With the rapid development of Unmanned Aerial Vehicle (UAV) systems, the autonomous landing of a UAV on a moving Unmanned Ground Vehicle (UGV) has received extensive attention as a key technology. At present, this technology is confronted with such problems as operating in GPS-denied environments, a low accuracy of target location, the poor precision of the relative motion estimation, delayed control responses, slow processing speeds, and poor stability. To address these issues, we present a hybrid camera array-based autonomous landing UAV that can land on a moving UGV in a GPS-denied environment...Read More
With the popularization and wide application of drones in military and civilian fields, the safety of drones must be considered. At present, the failure and drop rates of drones are still much higher than those of manned aircraft. Therefore, it is imperative to improve the research on the safe landing and recovery of drones. However, most drone navigation methods rely on global positioning system (GPS) signals. . .Read More
An infrared sensor is a commonly used imaging device. Unmanned aerial vehicles, the most promising moving platform, each play a vital role in their own field, respectively. However, the two devices are seldom combined in automatic ground vehicle detection tasks. Therefore, how to make full use of them—especially in ground vehicle detection based on aerial imagery–has aroused wide academic concern. . .Read More
Pedestrian detection is among the most frequently-used preprocessing tasks in many surveillance application fields, from low-level people counting to high-level scene understanding. Even though many approaches perform well in the daytime with sufficient illumination, pedestrian detection at night is still a critical and challenging problem for video surveillance systems. .Read More
Cross-domain image matching, which investigates the problem of searching images across different visual domains such as photo, sketch or painting, has attracted intensive attention in computer vision due to its widespread application. Unlike intra-domain matching, cross-domain images appear quite different in various characteristics.Read More
Robust extraction of consensus sets from noisy data is a fundamental problem in robot vision. Existing multimodel estimation algorithms have shown success on large consensus sets estimations. One remaining challenge is to extract small consensus sets in cluttered multimodel data set. In this article, we present an effective multimodel extraction method to solve this challenge.Read More
Discriminant analysis is an important and well-studied algorithm in pattern recognition area, and many linear discriminant analysis methods have been proposed over the last few decades. However, in the previous works, the between-scatter matrix is not updated when seeking the discriminant vectors, which causes redundancy for the well separated pairs.Read More
Real-time and high performance occluded object imaging is a big challenge to many computer vision applications. In recent years, camera array synthetic aperture theory proves to be a potential powerful way to solve this problem.Read More
This paper proposes a novel infrared camera array guidance system with capability to track and provide real time position and speed of a fixed-wing Unmanned air vehicle (UAV) during a landing process. The system mainly include three novel parts: (1) Infrared camera array and near infrared laser lamp based cooperative long range optical imaging module;Read More
Vehicle surveillance of a wide area allows us to learn much about the daily activities and traffic information. With the rapid development of
Diverse scene stitching is a challenging task in aerial video surveillance. This paper presents a hybrid stitching method based on the observation that aerial videos captured in real surveillance settings are neither totally ordered nor completely unordered.Read More
Heavy occlusions in cluttered scenes impose significant challenges to many computer vision applications. Recent light field imaging systems provide new see-through capabilities through synthetic aperture imaging (SAI) to overcome the occlusion problem.Read More
Linear Discriminant Analysis (LDA) is one of the most popular methods for dimension reduction. However, it suffers from class separation problem for C-class when the reduced dimensionality is less than C−1. To cope with this problem, we propose a subset improving method in this paper.Read More
The highly efficient and robust stitching of aerial video captured by unmanned aerial vehicles (UAVs) is a challenging problem in the field of robot vision. Existing commercial image stitching systems have seen success with offline stitching tasks, but they cannot guarantee high-speed performance when dealing with online aerial video sequences.Read More
This paper presents a novel real-time multiple object tracking algorithm, which contains three parts: region correlation based foreground segmentation, merging-splitting based data association and greedy searching based occluded object localization.Read More
Foreground detection is an important research problem in visual surveillance. In this paper, we present a novel multiple layer background model to detect and classify foreground into three classes, moving object, static object and ghost. The background is divided into two layers, reference background and dynamic background.Read More
Heavy occlusions in cluttered scenes impose significant challenges to many computer vision applications. Recent light field imaging systems provide new see-through capabilities through synthetic aperture imaging (SAI) to overcome the occlusion problem.
This work presents a real-time system for multiple object tracking in dynamic scenes. A unique characteristic of the system is its ability to cope with long-duration and complete occlusion without a prior knowledge about the shape or motion of objects. The system produces good segment and tracking results at a frame rate of 15-20 fps for image size of 320x240...
DOTS (Dynamic Object Tracking System) is an indoor, real-time, multi-camera surveillance system, deployed in a real office setting. DOTS combines video analysis and user interface components to enable security personnel to effectively monitor views of interest and to perform tasks such as tracking a person.
This work presents an active learning based method for pedestrian detection in complicated real-world scenes. Through analyzing the distribution of all positive and negative samples under every possible feature, a highly efficient weak classifier selection method is presented. Moreover, a novel boosting architecture is given to get satisfied False Positive Rate (FPR) and False Negative Rate (FNR) with few weak classifiers.
Multiple pedestrian tracking is regarded as a challenging work due to difficulties of occlusion, abrupt motion and changes in appearance. In this paper, we propose a multi-layer graph based data association framework to address occlusion problem. Our framework is hierarchical with three association layers and each layer has its corresponding association method.Read More