Moving Vehicle Detection of Airborne Video on SURF

2013 ◽  
Vol 734-737 ◽  
pp. 2815-2818
Author(s):  
Hui Liu ◽  
Chun Xian Gao ◽  
Xing Hao Ding ◽  
Zhe Zeng
Keyword(s):  
Detection Efficiency ◽  
Vehicle Detection ◽  
Detection Methods ◽  
Normal Vector ◽  
Detection Accuracy ◽  
External Interference ◽  
Moving Vehicle ◽  
Low Level ◽  
Wide Range ◽  
Moving Vehicle Detection

Due to the high mobility, a wide range of monitoring, air mobile platform-based vehicle detection and tracking system is becoming core of the investigation and the monitoring. Self-motion of the camera and external interference caused by the low-level platform led to instability of the obtained video and affect the correct detection of moving targets and subsequent analysis. For the characteristics for low-level video, an image stabilization algorithm based on SURF combined with normal vector of optical flow is proposed to solve moving vehicle detection low-altitude video. From the experimental results can be seen: (1) compared to other moving vehicle detection methods, the method proposed can get better detection efficiency and detection accuracy; (2) in the complex context, this method can effectively detect moving vehicles. The experiments show that this method has some theoretical and application value of space-based video moving target detection.

A Fast Forward Vehicle Detection Method by Multiple Vision Clues Fusion

2014 ◽  
Vol 543-547 ◽  
pp. 2647-2651
Author(s):  
Tai Qi Wu ◽  
Ye Zhang ◽  
Bin Bin Wang ◽  
Jia Heng Yu ◽  
De Wei Zhu
Keyword(s):  
Detection Method ◽  
System Development ◽  
Warning System ◽  
Vehicle Detection ◽  
Detection Methods ◽  
Vehicle Body ◽  
Detection Accuracy ◽  
Collision Warning ◽  
Moving Vehicle ◽  
Forward Collision Warning

With the development of intelligent vehicle technology, vehicle detection based on vision analysis has become an research hotspot in forward collision warning system development. Aiming to solve the existing problems in the current vehicle detection methods, for example, the detection accuracy is sensitive to the variation of illumination and object angle, we propose a forward moving vehicle detection method according to multiple vision clues fusion. Firstly, we locate the rough position using vehicle bottom shadow detection. The shadow is detected using an adaptive threshold image segmentation approach twice. Secondly, the symmetry of vehicle body and the perspective of camera field of view are both referenced to remove the inaccurate location in the first stage. The proposed method has been tested on several videos recorded in real urban conditions. Experimental results show that our method achieves 93.67% average detection accuracy in daytime, and its processing speed is more than 25fps. The proposed method has certain application prospects for improving the vision based forward collision warning system performance.

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

scholarly journals Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery

2018 ◽  
Vol 10 (12) ◽  
pp. 1987 ◽  
Author(s):  
Rocío Ramos-Bernal ◽  
René Vázquez-Jiménez ◽  
Raúl Romero-Calcerrada ◽  
Patricia Arrogante-Funes ◽  
Carlos Novillo
Keyword(s):  
Change Detection ◽  
Vegetation Index ◽  
Thermal Emission ◽  
Principal Component ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Types ◽  
Statistical Parameters ◽  
Wide Range ◽  
The World

Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work.

Adaptive Moving Vehicle Detection Algorithm Based on Hybrid Background Subtraction and Frame Difference

2014 ◽  
Vol 644-650 ◽  
pp. 930-933 ◽  
Author(s):  
Yan Li Luo ◽  
Han Lin Wan ◽  
Li Xia Xue ◽  
Qing Bin Gao
Keyword(s):  
Background Subtraction ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Target Image ◽  
Moving Vehicle ◽  
Frame Difference ◽  
Video Frequency ◽  
Background Difference ◽  
Moving Vehicle Detection ◽  
Initial Target

This paper proposes an adaptive moving vehicle detection algorithm based on hybrid background subtraction and frame difference. The background image of continuous video frequency is reconstructed by calculating the maximun probability grayscale using grey histogram; Moving regions is gained by frame defference, the initial target image is obtained by background difference method,moving regions image and initial target image AND,XOR and OR operations to get the vehicle moving target images. Experimental results show that the algorithm can response timely to the actual scene changes and improve the quality of moving vehicle detection.

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