We propose a novel oriented edge detection method called Difference of Shifted Image (DoSI) which has only subtractions between neighborhood pixels using padding-based shifting operation. Firstly, we can more quickly extract an oriented edge component in each direction from 8-neighborhoods using DoSI because there are no multiplications. Then, we can make a final edge map using all edge components by taking maximum value per each pixel. Moreover, we propose various types of oriented edge operators based on the Prewitt, Sobel and Laplacian. They are achieved by combinations of some oriented edge components obtained from DoSI. They have similar performance to existing edge operators based on convolution operations and also their procedures can be implemented in parallel. The experimental results show that the proposed edge detection methods requires less computation time than convolution-based methods and most of them are similar in edge description ability to the existing oriented edge operators.
Most of the human detection methods are using HOG (Histogram of Oriented Gradients). In the case of fixed camera environment, it is possible to make background model using GMM (Gaussian mixture model) and easily extract motions using background subtraction. However, it is difficult to recognize pedestrians among extracted motions. In this paper, we propose an efficient coarse-to-fine pedestrian detection framework which combines motion detection and HOG cascade to make a faster pedestrian detector. Firstly, motion detection is used as the coarse detection in order to reduce the area of interest to be covered by the pedestrian detector. Then HOG cascade which detects pedestrians is executed only on the blobs or ROIs selected from the coarse detection. The experimental results on PET2009 768X576 dataset show that proposed method of which processing speed is 11.46 fps is 7.5 times faster than HOG and 2.2 times faster than HOG cascade.