Optical flow based speed estimation in AUV target tracking

2002 ◽  
Author(s):  
Yang Fan ◽  
A. Balasuriya
Keyword(s):  
Optical Flow ◽  
Target Tracking ◽  
Speed Estimation

scholarly journals Multiple-Object-Tracking Algorithm Based on Dense Trajectory Voting in Aerial Videos

2019 ◽  
Vol 11 (19) ◽  
pp. 2278
Author(s):  
Tao Yang ◽  
Dongdong Li ◽  
Yi Bai ◽  
Fangbing Zhang ◽  
Sen Li ◽  
...  
Keyword(s):  
Optical Flow ◽  
Object Tracking ◽  
Target Tracking ◽  
Multiple Object Tracking ◽  
Tracking Algorithm ◽  
Frame Rate ◽  
Dense Optical Flow ◽  
Multiple Object ◽  
Dense Trajectory ◽  
Low Frame Rate

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. The method models the multiple-target-tracking problem as a voting problem of the dense-optical-flow trajectory to the target ID, which can be applied to aerial-surveillance scenes and is robust to low-frame-rate videos. More specifically, we first built an aerial video dataset for vehicle targets, including a training dataset and a diverse test dataset. Based on this, we trained the neural network model by using a deep-learning method to detect vehicles in aerial videos. Thereafter, we calculated the dense optical flow in adjacent frames, and generated effective dense-optical-flow trajectories in each detection bounding box at the current time. When target IDs of optical-flow trajectories are known, the voting results of the optical-flow trajectories in each detection bounding box are counted. Finally, similarity between detection objects in adjacent frames was measured based on the voting results, and tracking results were obtained by data association. In order to evaluate the performance of this algorithm, we conducted experiments on self-built test datasets. A large number of experimental results showed that the proposed algorithm could obtain good target-tracking results in various complex scenarios, and performance was still robust at a low frame rate by changing the video frame rate. In addition, we carried out qualitative and quantitative comparison experiments between the algorithm and three state-of-the-art tracking algorithms, which further proved that this algorithm could not only obtain good tracking results in aerial videos with a normal frame rate, but also had excellent performance under low-frame-rate conditions.

Moving Target Tracking Using Sparse Optical Flow Method

2013 ◽  
Vol 718-720 ◽  
pp. 2335-2339
Author(s):  
Tian Ding Chen ◽  
Jian Hu ◽  
Chao Lu ◽  
Zhong Jiao He
Keyword(s):  
Optical Flow ◽  
Target Tracking ◽  
New Method ◽  
Moving Target ◽  
Tracking Accuracy ◽  
Experimental Conditions ◽  
Moving Target Tracking ◽  
Research Production ◽  
Harris Corner ◽  
Comparison Results

Moving target tracking is a hot research spot of computer vision and applied in various fields. In this paper, a new tracking method base on sparse optical flow is put forward. In this method, targets are tracked through calculating the movements of Harris corner points, rather than the movements of all pixel points. Experiments results show that the tracking effect of this new method is pretty good. Tracking accuracy can reach more than 80% in most experimental conditions. And according to other peoples research production, experiments based on dense optical flow are done to compare with the new method proposed in this paper. The comparison results show that the new method has high calculation efficiency. This indicates that the method has feasibility and practical value.

scholarly journals Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks

2019 ◽  
Vol 11 (6) ◽  
pp. 123
Author(s):  
Huanan Dong ◽  
Ming Wen ◽  
Zhouwang Yang
Keyword(s):  
Optical Flow ◽  
Camera Calibration ◽  
Absolute Error ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Convolutional Network ◽  
Video Footage ◽  
Convolutional Networks ◽  
Calibration Methods ◽  
Non Local

Vehicle speed estimation is an important problem in traffic surveillance. Many existing approaches to this problem are based on camera calibration. Two shortcomings exist for camera calibration-based methods. First, camera calibration methods are sensitive to the environment, which means the accuracy of the results are compromised in some situations where the environmental condition is not satisfied. Furthermore, camera calibration-based methods rely on vehicle trajectories acquired by a two-stage tracking and detection process. In an effort to overcome these shortcomings, we propose an alternate end-to-end method based on 3-dimensional convolutional networks (3D ConvNets). The proposed method bases average vehicle speed estimation on information from video footage. Our methods are characterized by the following three features. First, we use non-local blocks in our model to better capture spatial–temporal long-range dependency. Second, we use optical flow as an input in the model. Optical flow includes the information on the speed and direction of pixel motion in an image. Third, we construct a multi-scale convolutional network. This network extracts information on various characteristics of vehicles in motion. The proposed method showcases promising experimental results on commonly used dataset with mean absolute error (MAE) as 2.71 km/h and mean square error (MSE) as 14.62 .

High-speed target tracking by fuzzy hostility-induced segmentation of optical flow field

2009 ◽  
Vol 9 (1) ◽  
pp. 126-134 ◽  
Author(s):  
Siddhartha Bhattacharyya ◽  
Ujjwal Maulik ◽  
Paramartha Dutta
Keyword(s):  
Flow Field ◽  
Optical Flow ◽  
Target Tracking ◽  
High Speed ◽  
Optical Flow Field

scholarly journals Research on Moving Target Tracking Based on FDRIG Optical Flow

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1122
Author(s):  
Gong ◽  
Wang
Keyword(s):  
Optical Flow ◽  
Target Tracking ◽  
Research Work ◽  
Flow Equation ◽  
Superior Performance ◽  
Moving Target ◽  
Moving Target Tracking ◽  
Motion Sequence ◽  
Tracking Model ◽  
Vehicle Motion

Aiming at the problem of moving target recognition, a moving target tracking model based on FDRIG optical flow is proposed. First, the optical flow equation was analyzed from the theory of optical flow. Then, with the energy functional minimization, the FDRIG optical flow technique was proposed. Taking a road section of a university campus as an experimental section, 30 vehicle motion sequence images were considered as objects to form a vehicle motion sequence image with a complex background. The proposed FDRIG optical flow was used to calculate the vehicle motion optical flow field by the Halcon software. Comparable with the classic Horn and Schunck (HS) and Lucas and Kande (LK) optical flow algorithm, the monitoring results proved that the FDRIG optical flow was highly precise and fast when tracking a moving target. The Ettlinger Tor traffic scene was then taken as the second experimental object; FDRIG optical flow was used to analyze vehicle motion. The superior performance of the FDRIG optical flow was further verified. The whole research work shows that FDRIG optical flow has good performance and speed in tracking moving targets and can be used to monitor complex target motion information in real-time.

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