scholarly journals Hybrid Video Stabilization for Mobile Vehicle Detection on SURF in Aerial Surveillance

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Gao Chunxian ◽  
Zeng Zhe ◽  
Liu Hui
Keyword(s):  
Vehicle Detection ◽  
Emergency Evacuation ◽  
Video Sequences ◽  
Video Stabilization ◽  
Camera Motion ◽  
Aerial Surveillance ◽  
Low Contrast ◽  
Moving Vehicle ◽  
Mobile Vehicle ◽  
Moving Vehicles

Detection of moving vehicles in aerial video sequences is of great importance with many promising applications in surveillance, intelligence transportation, or public service applications such as emergency evacuation and policy security. However, vehicle detection is a challenging task due to global camera motion, low resolution of vehicles, and low contrast between vehicles and background. In this paper, we present a hybrid method to efficiently detect moving vehicle in aerial videos. Firstly, local feature extraction and matching were performed to estimate the global motion. It was demonstrated that the Speeded Up Robust Feature (SURF) key points were more suitable for the stabilization task. Then, a list of dynamic pixels was obtained and grouped for different moving vehicles by comparing the different optical flow normal. To enhance the precision of detection, some preprocessing methods were applied to the surveillance system, such as road extraction and other features. A quantitative evaluation on real video sequences indicated that the proposed method improved the detection performance significantly.

A Quick Extraction Algorithm Using Window-Scanning for Moving Vehicles

2013 ◽  
Vol 756-759 ◽  
pp. 3879-3883
Author(s):  
Ji Ze Yang ◽  
Tie Sheng Fan
Keyword(s):  
Initial Point ◽  
Traffic Monitoring ◽  
Video Sequences ◽  
Moving Vehicle ◽  
Space Efficiency ◽  
Distribution Probability ◽  
Moving Vehicles ◽  
Extraction Algorithm ◽  
Stationary Location ◽  
Vehicle Movement

Aiming at the particularity of traffic monitoring video sequences and the regularity of vehicle movement, a quick extraction algorithm using window-scanning for moving vehicles in traffic monitoring videos is proposed in this paper. This algorithm uses hypothesis testing to higher order statistics of frame differences to achieve the rough separation of moving vehicles and background. Then obtain the length of the vehicle and extract the vertical coordinates of the initial point of moving vehicle by setting a static window with a stationary location, combining with the velocity of the vehicle and the moving pixel distribution probability in the window. And obtain the width of the vehicle the horizontal coordinates of the initial point of moving vehicle by setting a dynamic window, combining with the distribution probability of moving pixels in the window. Finally this algorithm achieved the quick extraction of vehicles with the window obtained by length and width, combining with the coordinates of the initial point of moving vehicle. Experiments show the feasibility of the algorithm that the time and space efficiency is relatively high.

scholarly journals Improvement in Target Range Estimation and the Range Resolution Using Drone

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1136 ◽  
Author(s):  
Kwan Hyeong Lee
Keyword(s):  
Detection Rate ◽  
Traffic Accidents ◽  
Vehicle Detection ◽  
Target Range ◽  
Vehicle Speed ◽  
Moving Vehicle ◽  
Range Resolution ◽  
The Road ◽  
Moving Vehicles ◽  
On The Road

This study measured the speed of a moving vehicle in multiple lanes using a drone. The existing methods for measuring a vehicle’s speed while driving on the road measure the speed of moving automobiles by means of a sensor that is mounted on a structure. In another method, a person measures the speed of a vehicle at the edge of a road using a speed-measuring tool. The existing method for measuring a vehicle’s speed requires the installation of a gentry-structure; however, this produces a high risk for traffic accidents, which makes it impossible to measure a vehicle’s speed in multiple lanes at once. In this paper, a method that used a drone to measure the speed of moving vehicles in multiple lanes was proposed. The suggested method consisted of two LiDAR sets mounted on the drone, with each LiDAR sensor set measuring the speed of vehicles moving in one lane; that is, estimating the speed of moving vehicles in multiple lanes was possible by moving the drone over the road. The proposed method’s performance was compared with that of existing equipment in order to measure the speed of moving vehicles using the manufactured drone. The results of the experiment, in which the speed of moving vehicles was measured, showed that the Root Mean Square Error (RMSE) of the first lane and the second lane was 3.30 km/h and 2.27 km/h, respectively. The vehicle detection rate was 100% in the first lane. In the second lane, the vehicle detection rate was 94.12%, but the vehicle was not detected twice in the experiment. The average vehicle detection rate is 97.06%. Compared with the existing measurement system, the multi-lane moving vehicle speed measurement method that used the drone developed in this study reduced the risk of accidents, increased the convenience of movement, and measured the speed of vehicles moving in multiple lanes using a drone. In addition, it was more efficient than current measurement systems because it allowed an accurate measurement of speed in bad environmental conditions.

scholarly journals Moving Vehicle Detection and Recognition Technology based on Artificial Intelligence

2022 ◽  
Vol 16 ◽  
pp. 399-405
Author(s):  
Zengfang Shi ◽  
Meizhou Liu
Keyword(s):  
Artificial Intelligence ◽  
Motion Vector ◽  
Texture Features ◽  
Vehicle Detection ◽  
Target Area ◽  
Moving Vehicle ◽  
Car Detection ◽  
Moving Vehicles ◽  
Moving Vehicle Detection ◽  
Detection And Recognition

The existing target detection and recognition technology has the problem of fuzzy features of moving vehicles, which leads to poor detection effect. A moving car detection and recognition technology based on artificial intelligence is designed. The point operation is adopted to enhance the high frequency information of the image, increase the image contrast, and delineate the video image tracking target. The motion vector similarity is used to predict the moving target area in the next frame of the image. The texture features of the moving car are extracted by artificial intelligence, and the center moment is calculated by the gray histogram distribution curve, the edge feature extraction algorithm is used to set the detection and recognition mode. Experimental results: under complex conditions, this design technology, compared with the other two kinds of moving vehicle detection and recognition technology, detected three more moving vehicles, which proved that the application prospect of the moving vehicle detection and recognition technology integrated with artificial intelligence is broader.

A Novel AMS-DAT Algorithm for Moving Vehicle Detection in a Satellite Video

2020 ◽  
pp. 1-5
Author(s):  
Xu Chen ◽  
Haigang Sui ◽  
Jian Fang ◽  
Mingting Zhou ◽  
Chen Wu
Keyword(s):  
Vehicle Detection ◽  
Moving Vehicle ◽  
Moving Vehicle Detection
Sign in / Sign up

Export Citation Format

Share Document