Detection by detections: Non-parametric detector adaptation for a video

We propose an approach to improving the detection results of a generic offline trained detector on a specific video. Our method does not leverage visual tracking as most detection by tracking methods do. Instead, the proposed detection by detections approach can serve as a more confident initialization for detection by tracking methods. Different from other supervised detector adaptation methods, we constrain the task to videos and no supervised labels for the target video are required for the adaptation; we intend to fill the gap between detection by tracking and pure detection by frames. As a non-parametric detector adaptation method, confident detections are collected to re-rank and to group other detections. We focus on methods with high precision detection results since it is necessitated in real application. Extensive experiments with two state-of-the-art detectors demonstrate the efficacy of our approach.