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Hello,

I am working on a project for detecting traffic lights in real world environment. I have trained a Haar Cascade classifier which tries to find Traffic Lights in a pre-defined Region Of Interest(30% from left and right is cropped and 10% from top and 50% from bottom). I use the color segmentation to find which color is present on detected traffic light and so far it works fine. However, i have a problem in finding the patterns of traffic lights, i.e. there are cases of left or right arrow present rather than just red and green colors. Please check the attached images for better understanding. We also used the Aspect Ratio of a detected traffic light (by detecting contours from a binary image) to differentiate between 3 pattern and 4 pattern of traffic lights but it doesn't work as expected. Could you suggest an efficient way of distinguishing 3-bulb traffic light from a 4-bulb one? I am trying to port it on an embedded system, so my computational resources are very limited. Thank you in advance.C:\fakepath\1.png(/upfiles/15701686466786052.png)

Hello,

I am working on a project for detecting traffic lights in real world environment. I have trained a Haar Cascade classifier which tries to find Traffic Lights in a pre-defined Region Of Interest(30% from left and right is cropped and 10% from top and 50% from bottom). I use the color segmentation to find which color is present on detected traffic light and so far it works fine. However, i have a problem in finding the patterns of traffic lights, i.e. there are cases of left or right arrow present rather than just red and green colors. Please check the attached images for better understanding. We also used the Aspect Ratio of a detected traffic light (by detecting contours from a binary image) to differentiate between 3 pattern and 4 pattern of traffic lights but it doesn't work as expected. Could you suggest an efficient way of distinguishing 3-bulb traffic light from a 4-bulb one? I am trying to port it on an embedded system, so my computational resources are very limited. Thank you in advance.C:\fakepath\1.png(/upfiles/15701686466786052.png)

Hello,

I am working on a project for detecting traffic lights in real world environment. I have trained a Haar Cascade classifier which tries to find Traffic Lights in a pre-defined Region Of Interest(30% from left and right is cropped and 10% from top and 50% from bottom). I use the color segmentation to find which color is present on detected traffic light and so far it works fine. However, i have a problem in finding the patterns of traffic lights, i.e. there are cases of left or right arrow present rather than just red and green colors. Please check the attached images for better understanding. We also used the Aspect Ratio of a detected traffic light (by detecting contours from a binary image) to differentiate between 3 pattern and 4 pattern of traffic lights but it doesn't work as expected. Could you suggest an efficient way of distinguishing 3-bulb traffic light from a 4-bulb one? I am trying to port it on an embedded system, so my computational resources are very limited. Thank you in advance.C:\fakepath\1.png C:\fakepath\2.png

Hello,

I am working on a project for detecting traffic lights in real world environment. I have trained a Haar Cascade classifier which tries to find Traffic Lights in a pre-defined Region Of Interest(30% from left and right is cropped and 10% from top and 50% from bottom). I use the color segmentation to find which color is present on detected traffic light and so far it works fine. However, i have a problem in finding the patterns of traffic lights, i.e. there are cases of left or right arrow present rather than just red and green colors. Please check the attached images for better understanding. We also used the Aspect Ratio of a detected traffic light (by detecting contours from a binary image) to differentiate between 3 pattern and 4 pattern of traffic lights but it doesn't work as expected. Could you suggest an efficient way of distinguishing 3-bulb traffic light from a 4-bulb one? I am trying to port it on an embedded system, so my computational resources are very limited. Thank you in advance.C:\fakepath\1.png C:\fakepath\2.pngadvance.

Hello,

I am working on a project for detecting traffic lights in real world environment. I have trained a Haar Cascade classifier which tries to find Traffic Lights in a pre-defined Region Of Interest(30% from left and right is cropped and 10% from top and 50% from bottom). I use the color segmentation to find which color is present on detected traffic light and so far it works fine. However, i have a problem in finding the patterns of traffic lights, i.e. there are cases of left or right arrow present rather than just red and green colors. Please check the attached images for better understanding. We also used the Aspect Ratio of a detected traffic light (by detecting contours from a binary image) to differentiate between 3 pattern and 4 pattern of traffic lights but it doesn't work as expected. Could you suggest an efficient way of distinguishing 3-bulb traffic light from a 4-bulb one? I am trying to port it on an embedded system, so my computational resources are very limited. Thank you in advance.