My friend and I did something similar using Haar Cascades for vehicle detection and Hough transforms to detect the lane lines. We used that to analyze dash cam videos and calculate following distance between the cars in the video.
The final product is here, http://asherman.site/hazcam/ and I also release the trained haar classifier separately on my own GitHub in case anyone had another useful projects for it. It was a lot of work hand selecting and cropping 400+ photos of the rear ends of cars, so I figured I might as well share the result. https://github.com/pddenhar/OpenCV-Dashcam-Car-Detection
This looks like it might be exactly what I'm looking for. I am quadriplegic and use a powered wheelchair, I also live in a small town with narrow roads and can't tell how far away a car is from behind me. I can't use regular wing mirrors as it would make the wheelchair to wide and I can't quite get the angle on a mirror to see behind me, so I thought I would use a camera on the back of the headrest which with some computer type cleverness™ could tell me if about to be mown over.
I was recently hit by a Mercedes SLK, and it's not something I'm anxious to repeat.
At the moment I'm thinking a GoPro streaming wirelessly to a Raspberry Pi, which can do the visual processing and essentially warn me if something big and metal is coming up behind me too quickly. I got the hardware, but I really don't have the software chops so if someone could point me in the right direction I would really appreciate it!
By all means I respect wanting to roll your own version of this. But if you would like to buy something to detect vehicles from your rear, there is something that already exists and is used commonly by cyclist. You may however need a bike computer as well, so it might get a bit expensive...
How about use the PI Camera module instead? No wireless mess and much cheaper than a GoPro. I'm not even sure the GoPro video stream is accessible outside their proprietary app.
There are a lot of tutorials online on developing computer vision with the PI.
However, it sounds like the K.I.S.S. solution for you would be a backup camera or something so you can see whats happening behind you instead of relying on CV (computer vision) especially if you don't have the software chops to take on the CV challenge.
Chapter five of "OpenCV for Secret Agents" has a night-time version that recognizes headlights. The author knows OpenCV and I found the style of the book to be less dry than your usual Packt tutorial.
This is one of the projects in Udacity Self-Driving Car Nanodegree in term 1. I've done it. It's simple and does not need deep learning. But if you want to go fancy, you can use image segmentation. http://blog.qure.ai/notes/semantic-segmentation-deep-learnin...
The HOG + SVM method is quite slow and not as accurate as a deep learning approach. Before jumping onto semantic segmentation, I recommend re-implementing this project or more generally solve this problem using a Regional Convolution Neural Network architecture (R-CNN) like Faster R-CNN[1] or YOLO[2] for instance.
This might be the wrong place to ask, but my search skills are failing me as I try to understand what "Space binning" (referred to in the README of the linked project) is. The top hits seem to all point back to the original article.
I noticed you are standardizing your dataset before your test/train split. This is an example of information leakage which is causing your model to overfit by learning the test example distribution: http://www.eggie5.com/97-model-evaluation-information-leakin...
[+] [-] quasse|8 years ago|reply
The final product is here, http://asherman.site/hazcam/ and I also release the trained haar classifier separately on my own GitHub in case anyone had another useful projects for it. It was a lot of work hand selecting and cropping 400+ photos of the rear ends of cars, so I figured I might as well share the result. https://github.com/pddenhar/OpenCV-Dashcam-Car-Detection
[+] [-] escapologybb|8 years ago|reply
I was recently hit by a Mercedes SLK, and it's not something I'm anxious to repeat.
At the moment I'm thinking a GoPro streaming wirelessly to a Raspberry Pi, which can do the visual processing and essentially warn me if something big and metal is coming up behind me too quickly. I got the hardware, but I really don't have the software chops so if someone could point me in the right direction I would really appreciate it!
Thanks!
[+] [-] radix07|8 years ago|reply
https://www.amazon.com/Garmin-Varia-Rearview-Radar-Light/dp/...
[+] [-] nkristoffersen|8 years ago|reply
There are a lot of tutorials online on developing computer vision with the PI.
However, it sounds like the K.I.S.S. solution for you would be a backup camera or something so you can see whats happening behind you instead of relying on CV (computer vision) especially if you don't have the software chops to take on the CV challenge.
[+] [-] coredog64|8 years ago|reply
[+] [-] Nilef|8 years ago|reply
[+] [-] langitbiru|8 years ago|reply
[+] [-] edshiro|8 years ago|reply
The HOG + SVM method is quite slow and not as accurate as a deep learning approach. Before jumping onto semantic segmentation, I recommend re-implementing this project or more generally solve this problem using a Regional Convolution Neural Network architecture (R-CNN) like Faster R-CNN[1] or YOLO[2] for instance.
[1]: https://arxiv.org/abs/1506.01497 [2]: https://arxiv.org/abs/1506.02640
[+] [-] abhinavkulkarni|8 years ago|reply
[+] [-] garyfirestorm|8 years ago|reply
[+] [-] Winterflow3r|8 years ago|reply
[+] [-] yelnatz|8 years ago|reply
[+] [-] ivarv|8 years ago|reply
[+] [-] eggie5|8 years ago|reply
[+] [-] colmvp|8 years ago|reply
Second paragraph - 'argures' should be 'argues', and 'resonse' should be 'response'.