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Good work! I did this project as well about a week ago. I will shamelessly also share my writings on this project: https://medium.com/towards-data-science/teach

Good work! I did this project as well about a week ago. I will shamelessly also share my writings on this project: https://medium.com/towards-data-science/teaching-cars-to-see-vehicle-detection-using-machine-learning-and-computer-vision-54628888079a https://medium.com/towards-data-science/teaching-cars-to-see... . 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 https://arxiv.org/abs/1506.01497 [2]: https://arxiv.org/abs/1506.02640 https://arxiv.org/abs/1506.02640

lxtx 9y ago
Totally agree with your point on HOG + SVM, I think it is obsoleted by convolutional neural networks. I wrote a realtime human detection library [1] for a robotics project that used HOG + a simple neural net for classification. While it worked okay, I wasn't happy with the precision (around 90%) and decided to try out a simple convnet from Torch (doing the classication on depth images instead of HOG descriptors). The Torch version was slightly slower on a CPU, but both the precision and recall jumped up drastically. [1]: https://github.com/seemk/FastHumanDetection https://github.com/seemk/FastHumanDetection