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Background subtraction algorithm
Calculation of Traffic congestion index
A method considered number and speed
Main Algorithms
Calculation of Traffic congestion index
Our Method
Firstly, this method uses discrete-frame difference algorithm to extract the images that have vehicle information.
Secondly, this method uses the histogram equalization algorithm to eliminate the noise of the images.
Finally, this method recognizes the vehicle from the video and computes the traffic congestion index by the calculation method based on discreteframe difference.
Experiments
Experiment of Images Processing
Time and effects Comparison
Experiment of Discrete-frame Method to Extract Traffic Images
Time testing
A method considered number and speed
W represents the total number of identified vehicles. k1 represents the total number of frames of video section containing vehicle information. Ci represents the number of identified vehicles in i frames in the video that has vehicle information. Wmax represents the maximum number of identified vehicles. S represents the length of the video detection region. Z represents the number of interval frames in discrete-frame algorithm. Tmax represents the total time of the video. t represents the difference of two frames. m represents the minimum length of a vehicle. ρy represents the traffic congestion index without unit conversion.
Main Algorithms
Image extraction method
Discrete-frame algorithm
Image de-noising method
Histogram equalization algorithm
Vehicle identification method
A Traffic-congestion Detection Method for Bad Weather Based on Traffic Video
Purpose
Current problem:
The result of traffic congestion weather is inaccurate
Experiment of Vehicle Identification
Accuracy testing
Results
Results
Baidu Nhomakorabea
Results
Thank you
detection in bad
Our purpose:
A Traffic-congestion Detection Method for Bad Weather
Introduction
Traffic congestion detection is of great significance in city vehicle, road designing, traffic lights setting, preventing traffic congestion and other application fields. Therefore, the traffic congestion detection plays an important role in transportation field. Unfortunately, the results of an automatic vehicle identification system for detecting traffic is prone to appear high false-negative rate in bad weather such as fog, mist, and rain. It may lead cause of errors in statistics of traffic congestion index and increases the risk or economic loss in relevant departments and industry.
Calculation of Traffic congestion index
A method considered number and speed
Main Algorithms
Calculation of Traffic congestion index
Our Method
Firstly, this method uses discrete-frame difference algorithm to extract the images that have vehicle information.
Secondly, this method uses the histogram equalization algorithm to eliminate the noise of the images.
Finally, this method recognizes the vehicle from the video and computes the traffic congestion index by the calculation method based on discreteframe difference.
Experiments
Experiment of Images Processing
Time and effects Comparison
Experiment of Discrete-frame Method to Extract Traffic Images
Time testing
A method considered number and speed
W represents the total number of identified vehicles. k1 represents the total number of frames of video section containing vehicle information. Ci represents the number of identified vehicles in i frames in the video that has vehicle information. Wmax represents the maximum number of identified vehicles. S represents the length of the video detection region. Z represents the number of interval frames in discrete-frame algorithm. Tmax represents the total time of the video. t represents the difference of two frames. m represents the minimum length of a vehicle. ρy represents the traffic congestion index without unit conversion.
Main Algorithms
Image extraction method
Discrete-frame algorithm
Image de-noising method
Histogram equalization algorithm
Vehicle identification method
A Traffic-congestion Detection Method for Bad Weather Based on Traffic Video
Purpose
Current problem:
The result of traffic congestion weather is inaccurate
Experiment of Vehicle Identification
Accuracy testing
Results
Results
Baidu Nhomakorabea
Results
Thank you
detection in bad
Our purpose:
A Traffic-congestion Detection Method for Bad Weather
Introduction
Traffic congestion detection is of great significance in city vehicle, road designing, traffic lights setting, preventing traffic congestion and other application fields. Therefore, the traffic congestion detection plays an important role in transportation field. Unfortunately, the results of an automatic vehicle identification system for detecting traffic is prone to appear high false-negative rate in bad weather such as fog, mist, and rain. It may lead cause of errors in statistics of traffic congestion index and increases the risk or economic loss in relevant departments and industry.