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Research on high-definition video vehicles location and tracking

Research on high-definition video vehicles location and tracking. Xiong Changzhen , LiLin IEEE , Distributed Computing and Applications to Business Engineering and Science (DCABES), 2010 Ninth International Symposium on. 1 、 Introduction.

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Research on high-definition video vehicles location and tracking

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  1. Research on high-definition video vehicles location and tracking XiongChangzhen, LiLin IEEE, Distributed Computing and Applications to Business Engineering and Science (DCABES), 2010 Ninth International Symposium on

  2. 1、 Introduction • high-definition(HD) video detection system can achieve high-definition monitoring to urban roads. • Managers can analyze clearly the road traffic condition and traffic flow vehicle character. • With the capability of intelligent analysis to high-definition images, it can achieve road traffic flow detection and incident detection.

  3. 1、 Introduction • The existing vehicle detection algorithm of standard-definition(SD) video mainly includes: • Optical flow method • Frame difference method • Edge detection method • Gaussian background modeling method • Bayesian background modeling method • Background modeling based on codebook method • this paper presents a vehicle tracking method based on image brightness.

  4. 2、HD (high-definition) video vehicledetection algorithm • The road can be divided into the lane area • Because interference between the adjacent lanes is relatively small. • Taking lane as a unit. • Lane detection can also be used to adaptively demarcate detection area. • The video with the resolution 2592 * 1936 is used in this experiment

  5. 2.1 Interest Region

  6. 2.2 Brightness Curve Scanning lanes by line along the vertical direction in an image, summing the brightness value in line.

  7. 2.2 Brightness Curve • Reflect the complete information • such as lane width, vehicle size, vehicle color (black or white), moving distance of vehicles in the image, elapsed time, etc. • Circle out a region in the grayscale image according to the direction of traffic flow • Denoisethe stored values (removing the values which is too large or too small) • Draw the curve.

  8. 2.3 Vehicle Location Input image • Take the absolute value after difference with the background value. • Adaptive Gaussian background modeling method Is initial background? no yes Pre-processing Adaptive Background production - Background Background update

  9. 2.3 Vehicle Location • Adaptive Background Extraction: d>T =>mark as uncertain background d<=T =>background Background updating others

  10. 2.3 Vehicle Location • we can get vehicle curve, as shown in the following formula

  11. 2.3 Vehicle Location convex part means the vehicle's location, and we can get that the number of vehicles in this frame in this lane is 2.

  12. 2.4 Vehicle Tracking • Count the number of vehicles. The match is taking upward position in our video • if frame i +1 has surplus vehicles in addition to them matching vehicles, then the surplus vehicles are new vehicles, number them from top to bottom • Number rules: number frame i from top to bottom in turn, the top vehicle is 1, the following vehicle is 2,3 • • •, the new vehicle in frame i +1 is numbered in succession • Do the same processing to another two lanes

  13. EXPERIMENTAL RESULTS • The video is taken by AV5105 5 Mega pixel IP-camera high-definition camera . CPU is Intel 1.83G dual-core Dell desktop

  14. EXPERIMENTAL RESULTS

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