Edited film alignment is the post-production process of finding small parts of unedited footage that temporally and spatially match an edited film. The huge amount of data to be processed makes significant downsampling of the videos essential in real-life applications. Simultaneously, professional users demand that the task be achieved with frame and pixel-level accuracy. We propose a novel selective Hough transform (SHT) and an accurate template matching method to address the difficult trade-off between accuracy and scalability. For robust temporal alignment, SHT investigates the selectivity of frame-level similarities and advantageously reduces the weights of mismatches. The template matching method encompasses spatial Hough transform and sum of squared differences (SSD) minimization. SSD is efficiently approximated by exploiting the second-order derivative of image intensity. Experiments conducted on real-world data show the superiority of our methods.