Active contours and optical flow for automatic tracking of flying vehicles

Tracking of moving objects in video streams with considering different dynamic backgrounds is a challenging problem in a real time dynamic environment. The proposed approach uses an observation model based on optical flow information used to know the displacement of the objects present in the scene. After finding the moving regions in the initial frame, we are applying active contour model (ACM) to track the moving objects in the further frames dynamically. These models have been used as a natural means of incorporating flow information into the tracking. The formulation of the active contour model by incorporating an additional force driven optical flow field improves the tracking speed. This algorithm efficiently works to track for low contrast videos like aerial videos.

Image based Lung cancer diagnosis offers an effective and non-intrusive method for identification and diagnosis of lung cancer in a patient. This is done by segmentation of lung nodules from radiological images obtained from patients. This paper proposes a framework to segment nodule lesions in images. The novelty of the proposed framework is that we use optical flow information along with the morphological operations on images. The proposed framework applies the Farneback method of optical flow extraction algorithms for the nodule segmentation task. This work validates and demonstrates that optical flow information is useful for nodule segmentation and can be used in tandom with other features.

Generation of a sequence of behaviors is necessary for the RoboCup Humanoid league to realize not simply an individual robot performance but also cooperative ones between robots. A typical example task is passing a ball between two humanoids, and the issues are: (1) basic skill decomposition, (2) skill learning, and (3) planning to connect the learned skills. This paper presents three methods for basic skill learning (trapping, approaching to, and kicking a ball) based on optic flow information by which a robot obtains sensorimotor mapping to realize the desired skill, assuming that skill decomposition and planning are given in advance. First, optic flow information of the ball is used to predict the trapping point. Next, the flow information caused by the self-motion is classified into the representative vectors, each of which is connected to motor modules and their parameters. Finally, optical flow for the environment caused by kicking motion is used to predict the ball trajectory after kicking. The experimental results are shown and discussion is given with future issues.

Image segmentation is a key topic in computer vision, serving as a pre-step in a number of robotics tasks, including object recognition, obstacle avoidance and topological localization. In the literature, image segmentation has been employed as auxiliary information in order to improve optical flow performance. In this work, an alternative approach is proposed, in which optical flow information is used to aid image segmentation, aiming at scene understading for mobile robots. The proposed system performs dense optical flow analysis, followed by clustering of the optical flow vectors in a four dimensional space (formed by the x and y positions, angle and magnitude of each vector). Results from the clustering are used as ‘seeds’ in the segmentation process, performed by watershed segmentation in our implementation. In addition, the flow ‘image’ is combined with the original image, generating an image better suited for watershed segmentation, reducing the local minima effect often seen in this type of segmentation algorithms. The main pipeline considers the use of multi-modality cameras (visible and thermal-infrared). Since they see substantially different information, multi-modality further improves the amount of features of the resulting flows. Experimental results in urban and semi-urban scenarios with efficient segmentation illustrate the applicability of the method.

This paper proposes a new algorithm for abnormal behavior detection in surveillance video. Firstly, the motion information image of each frame is constructed by calculating the optical flow size and the angle difference between the optical flow vectors between consecutive frames, and then the obtained motion image information is input into the convolutional neural network (CNN) for training, and used for video abnormal behavior detection. In the algorithm, the motion information image generated based on optical flow information can provide the motion information features in the video image more accurately, which makes it easier to distinguish the normal behavior and abnormal behavior of the video. The experiment of this algorithm is carried out on the commonly used data set PETS 2009. Experimental results show that the proposed method and other algorithms have a significant improvement in the accuracy of abnormal behavior detection.

In this paper, we present two new observation models based on optical flow information to track objects using particle filter algorithms. Although optical flow information enables us to know the displacement of objects present in a scene, it cannot be used directly to displace an object model since flow calculation techniques lack the necessary precision. In view of the fact that probabilistic tracking algorithms enable imprecise or incomplete information to be handled naturally, these models have been used as a natural means of incorporating flow information into the tracking.

In mining operation, measuring the speed of a belt conveyor is important for safe and efficient production. The contact measurement system is the mainstream choice for existing measuring speed systems, but the contact method cannot avoid some potential problems and hidden dangers in the running process of belt conveyor. A novel contactless measurement system based on optical flow techniques is designed in this study, which can calculate running speed of belt conveyor in real time. During the operation of belt conveyor, it can be found that most regions of the images are similar when the camera is set to high-speed continuous shooting mode. These similar image regions have a certain displacement change in the moving direction of the belt conveyor due to the time difference of its shooting, and can be described in a simplified way which is the image feature keypoint. The machine vision algorithm extracts image feature keypoints and acquires the optical flow information of image feature keypoints. Motion displacement information including the displacement change can be extracted by analyzing of optical flow information about captured images to calculate the running speed. The contactless measuring speed system is verified by experiments. Experimental results show that the measuring accuracy can be presented by indicators with RMSE of 0.042 m/s and MAE of 0.029 m/s.

In this paper, a vision based adaptive controller is proposed for the obstacle avoidance of a micro aerial vehicle (MAV) by using the optical flow information. In order to employ the optical flow for indicating the distance between the MAV and surrounding, the multi-thread processing algorithm is proposed to reliably obtain the optical flow information uniformly diffused in the whole image frame. The MAV system is treated as a switched system while manipulating the different modes during the obstacle avoidance task. Based on the desired flying direction of obstacle avoidance obtained by the optical flow estimation, we design an adaptive controller such that the desired trajectory can be tracked under different switching modes. The simulations present the tracking response of the adaptive controller of a switched system, and the experiments of the overall system validate the collision-avoidance performance of the MAV.

In this paper, a thorough research to the small unmanned aerial vehicle localization technology based on the fusion of optical flow and inertial navigation information has been conducted. An experiment platform based on Pixhawk and PX4FLOW is built to test the performance of optical flowAnertial localization system Experiment results shows that this system can work at both indoor and outdoor environment. In the indoor environment, the optical flowAnertial positioning system has better accuracy compared with the system using pure optical flow information. In the outdoor environment, the results proves that this system is a potential solution for UAV positioning in GPS-denied environment-with an error of 5 meters.

Precise estimation of optical flow, stereo depth and camera motion are important for the real-world 3D scene understanding and visual perception. Since the three tasks are tightly coupled with the inherent 3D geometric constraints, current studies have demonstrated that the three tasks can be improved through jointly optimizing geometric loss functions of several individual networks. In this paper, we show that effective feature-level collaboration of the networks for the three respective tasks could achieve much greater performance improvement for all three tasks than only loss-level joint optimization. Specifically, we propose a single network to combine and improve the three tasks. The network extracts the features of two consecutive stereo images, and simultaneously estimates optical flow, stereo depth and camera motion. The whole network mainly contains four parts: (I) a feature-sharing encoder to extract features of input images, which can enhance features' representation ability; (II) a pooled decoder to estimate both optical flow and stereo depth; (III) a camera pose estimation module which fuses optical flow and stereo depth information; (IV) a cost volume complement module to improve the performance of optical flow in static and occluded regions. Our method achieves state-of-the-art performance among the joint unsupervised methods, including optical flow and stereo depth estimation on KITTI 2012 and 2015 benchmarks, and camera motion estimation on KITTI VO dataset.

In this paper we try to develop an algorithm for visual obstacle avoidance of autonomous mobile robot. The input of the algorithm is an image sequence grabbed by an embedded camera on the B21r robot in motion. Then, the optical flow information is extracted from the image sequence in order to be used in the navigation algorithm. The optical flow provides very important information about the robot environment, like: the obstacles disposition, the robot heading, the time to collision and the depth. The strategy consists in balancing the amount of left and right side flow to avoid obstacles, this technique allows robot navigation without any collision with obstacles. The robustness of the algorithm will be showed by some examples.

Falsification of Human Faces using face photos has been an arising problem for face recognition and verification systems. In this paper, we propose a system to distinguish face photos from true faces by their motion models. In order to enhance the difference between the two classes, we design an enhanced optical flow method which generates a larger difference between the motion model of true faces and that of face photos. The feature vector we adopted is the dense optical flow field across a short period of time. An LDA-based training method is adopted to separate the projection of the training data into two classes, and a Bayes classifier is used to classify the testing samples. Under the specified motion of true faces and face photos, our proposed method can effectively distinguish the two classes with high verification rate. Even if the motion is arbitrary for both classes, the proposed system can also report satisfying results.

C-V model has the advantage of being able to detect boundaries of objects that are not defined by gradient. However, when detecting these types of edges, the C-V model only considers the average value of each region without local information. As a result, its segmentation result may exist errors, when detecting non-gradient defined boundaries. In order to overcome this problem, we modify the fitting term of classical C-V model with an extra weight. This weight can control the relative height of zero-level contour, so the new method can decrease the segmentation errors. Experiments show that the new model can obtain more accurate results and segment multi-phase images by setting proper weights. Active contour models (1), based on level set method, are one important type of methods in image segmentation. Those methods can handle topological changes automatically, and can get the boundaries of objects with complex edges. At present, there are two categories of active contour methods. One is based on the edge information and the other is based on the region information. Snakes (2), geometric active contours (3), geodesic active contours (GAC) model (4) are edge-based models. These methods get the boundaries of objects from the gradients of images. As these methods depend on the gradient information, when processing images with non-gradient defined edges, they will get wrong boundaries. While M-S model (5) and C-V model (6) are based on global region information. They take piecewise constant patches as the solution of segmentation problems. As a result, it can process the images whose edges are not well defined. Additionally, C-V model can process images fast and segment the objects with inner-boundaries, and its solution is robust to the initialization of the curves. However, C-V model may have problems when detecting blurred and fuzzy edges. The reason is that the C-V model only considers the averages inside and outside the zero-level contours, and the results determined by its energy functional may be not suitable for these edge types. In this paper, we propose an improved C-V model with a generalized fitting term to deal with the above problem. A weight factor w added into the generalized fitting term, controls the height of zero-level sets of the original C-V model. Thus we may obtain accurate segmentation results by

Automatic bone segmentation of Computed Tomography (CT) images is an important step in imageguided surgery where segmentation errors could be critical. Previous attempts include intensity-, edge-, region-, and deformable curve-based approaches [1], but none claims fully satisfactory performance. In this study, we have tested most widely used active contour (AC) -based approaches to segment knee bones from CT imagery, namely the Gradient Vector Flow (GVF) AC, the original geometric AC, the geodesic AC, the GVF fast geometric AC, and the Chan-Vese multiphase AC without edges. Among the techniques, only the Chan-Vese multiphase AC demonstrated satisfactory performance, proving its suitability for bone segmentation from CT images.

This study proposed a novel tracking algorithm for a mobile robot using modified CAMSHIFT and optical flow. CAMSHIFT algorithm is vulnerable to the lighting, brightness and speed of the object because it is based on the HSV color space model. To overcome the difference of light condition, the searching window of CAMSHIFT is set by SURF algorithm. Optical flow information is used to predict the position and velocity of the fast-moving object. The CAMSHIFT searching window in an image can be adjusted quickly. Furthermore, an ultra-sonic sensor is used to calculate the distance to an object. The proposed fusion algorithm shows superior performance in terms of accuracy, speed and reliability, compared with original SURF and CAMSIFT.