Improved deformable part model for object detection based on scale invariant feature transform

We propose an approach to improving the detection results of a generic offline trained detector on frames from a specific video. For two consecutive frames of a video with the object, deformable part model(DPM) detection is perform to get the original detections. Then respectively obtain the image patches corresponding to the detected root box and part boxes. Thirdly, extract scale invariant feature transform features(SIFT) from those image patches and match the sift features by KD-Tree. Finally, get the SIFT_DPM detection result of from the matches between image patches of continuous frames. We focus on methods with high precision detection results since it is necessitated in real application. Extensive experiments with state-of-the-art detector demonstrate the efficacy of our approach..